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ORCIDJürgen Schmidhuber
Person information
- affiliation: King Abdullah University of Science and Technology (KAUST), Saudi Arabia
- affiliation (former): University of Applied Sciences and Arts of Southern Switzerland, Switzerland
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2020 – today
- 2025
[e12]Djork-Arné Clevert
, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
AI in Drug Discovery - First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings. Lecture Notes in Computer Science 14894, Springer 2025, ISBN 978-3-031-72380-3 [contents]- 2024
- [j78]Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber:
Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter. IEEE Trans. Games 16(2): 384-395 (2024) - [j77]Lukas Tuggener, Raphael Emberger, Adhiraj Ghosh, Pascal Sager, Yvan Putra Satyawan, Javier A. Montoya-Zegarra, Simon Goldschagg, Florian Seibold, Urs Gut, Philipp Ackermann, Jürgen Schmidhuber, Thilo Stadelmann:
Real World Music Object Recognition. Trans. Int. Soc. Music. Inf. Retr. 7(1): 1-14 (2024) - [c255]Jinheng Xie, Songhe Deng, Bing Li, Haozhe Liu, Yawen Huang, Yefeng Zheng, Jürgen Schmidhuber, Bernard Ghanem, Linlin Shen, Mike Zheng Shou:
Tune-an-Ellipse: CLIP Has Potential to Find what you Want. CVPR 2024: 13723-13732 - [c254]Jürgen Schmidhuber:
Past, Present, Future, and Far Future of AI. DATA 2024: 9 - [c253]Kirolos Ataallah, Xiaoqian Shen, Eslam Abdelrahman, Essam Sleiman, Mingchen Zhuge, Jian Ding, Deyao Zhu, Jürgen Schmidhuber, Mohamed Elhoseiny:
Goldfish: Vision-Language Understanding of Arbitrarily Long Videos. ECCV (29) 2024: 251-267 - [c252]Qinhan Hou, Stefano van Gogh, Kevin Scheck, Zhao Ren, Tanja Schultz, Michael Wand, Jürgen Schmidhuber:
emg2vec: Self-supervised Pretraining in Electromyography-based Silent Speech Interfaces. EMBC 2024: 1-5 - [c251]Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert:
Curating Reagents in Chemical Reaction Data with an Interactive Reagent Space Map. AIDD@ICANN 2024: 21-35 - [c250]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Self-organising Neural Discrete Representation Learning à la Kohonen. ICANN (1) 2024: 343-362 - [c249]Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber:
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. ICLR 2024 - [c248]Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber:
Exploring the Promise and Limits of Real-Time Recurrent Learning. ICLR 2024 - [c247]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. ICML 2024 - [c246]Aditya A. Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. ICML 2024 - [c245]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. ICML 2024 - [c244]Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang:
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays. ICML 2024 - [c243]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
GPTSwarm: Language Agents as Optimizable Graphs. ICML 2024 - [c242]Renzo Caballero, Piotr Piekos, Eric Feron, Jürgen Schmidhuber:
Utilizing a Malfunctioning 3D Printer by Modeling Its Dynamics with Machine Learning. ICRA 2024: 15562-15569 - [c241]Jürgen Schmidhuber:
Past, Present, Future, and Far Future of AI. ICSOFT 2024: 9 - [e11]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part I. Lecture Notes in Computer Science 15016, Springer 2024, ISBN 978-3-031-72331-5 [contents] - [e10]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part II. Lecture Notes in Computer Science 15017, Springer 2024, ISBN 978-3-031-72334-6 [contents] - [e9]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part III. Lecture Notes in Computer Science 15018, Springer 2024, ISBN 978-3-031-72337-7 [contents] - [e8]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 15019, Springer 2024, ISBN 978-3-031-72340-7 [contents] - [e7]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part V. Lecture Notes in Computer Science 15020, Springer 2024, ISBN 978-3-031-72343-8 [contents] - [e6]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 15021, Springer 2024, ISBN 978-3-031-72346-9 [contents] - [e5]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 15022, Springer 2024, ISBN 978-3-031-72349-0 [contents] - [e4]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science 15023, Springer 2024, ISBN 978-3-031-72352-0 [contents] - [e3]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IX. Lecture Notes in Computer Science 15024, Springer 2024, ISBN 978-3-031-72355-1 [contents] - [e2]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part X. Lecture Notes in Computer Science 15025, Springer 2024, ISBN 978-3-031-72358-2 [contents] - [i168]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
Language Agents as Optimizable Graphs. CoRR abs/2402.16823 (2024) - [i167]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. CoRR abs/2403.11998 (2024) - [i166]Wentian Zhang, Haozhe Liu, Jinheng Xie, Francesco Faccio, Mike Zheng Shou, Jürgen Schmidhuber:
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models. CoRR abs/2404.02747 (2024) - [i165]Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber:
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms. CoRR abs/2404.08093 (2024) - [i164]Haozhe Liu, Wentian Zhang, Bing Li, Bernard Ghanem, Jürgen Schmidhuber:
Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable. CoRR abs/2405.00466 (2024) - [i163]Aditya A. Ramesh, Kenny Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. CoRR abs/2405.03878 (2024) - [i162]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber, Christopher Potts, Christopher D. Manning:
MoEUT: Mixture-of-Experts Universal Transformers. CoRR abs/2405.16039 (2024) - [i161]Anand Gopalakrishnan, Aleksandar Stanic, Jürgen Schmidhuber, Michael Curtis Mozer:
Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery. CoRR abs/2405.17283 (2024) - [i160]Yuhui Wang, Miroslav Strupl, Francesco Faccio, Qingyuan Wu, Haozhe Liu, Michal Grudzien, Xiaoyang Tan, Jürgen Schmidhuber:
Highway Reinforcement Learning. CoRR abs/2405.18289 (2024) - [i159]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. CoRR abs/2406.03485 (2024) - [i158]Yuhui Wang, Qingyuan Wu, Weida Li, Dylan R. Ashley, Francesco Faccio, Chao Huang, Jürgen Schmidhuber:
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. CoRR abs/2406.08404 (2024) - [i157]Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert:
Accelerating the inference of string generation-based chemical reaction models for industrial applications. CoRR abs/2407.09685 (2024) - [i156]Kirolos Ataallah, Xiaoqian Shen, Eslam Abdelrahman, Essam Sleiman, Mingchen Zhuge, Jian Ding, Deyao Zhu, Jürgen Schmidhuber, Mohamed Elhoseiny:
Goldfish: Vision-Language Understanding of Arbitrarily Long Videos. CoRR abs/2407.12679 (2024) - [i155]Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Mingchen Zhuge, Jürgen Schmidhuber, Xin Gao, Xiangliang Zhang:
ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering. CoRR abs/2407.16931 (2024) - [i154]Mingchen Zhuge, Changsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber:
Agent-as-a-Judge: Evaluate Agents with Agents. CoRR abs/2410.10934 (2024) - [i153]Haozhe Liu, Shikun Liu, Zijian Zhou, Mengmeng Xu, Yanping Xie, Xiao Han, Juan C. Pérez, Ding Liu, Kumara Kahatapitiya, Menglin Jia, Jui-Chieh Wu, Sen He, Tao Xiang, Jürgen Schmidhuber, Juan-Manuel Pérez-Rúa:
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale. CoRR abs/2410.20280 (2024) - [i152]Nanbo Li, Firas Laakom, Yucheng Xu, Wenyi Wang, Jürgen Schmidhuber:
FACTS: A Factored State-Space Framework For World Modelling. CoRR abs/2410.20922 (2024) - [i151]Vincent Herrmann, Dylan R. Ashley, Jürgen Schmidhuber:
Automatic Album Sequencing. CoRR abs/2411.07772 (2024) - 2023
- [j76]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers. Neural Comput. 35(4): 593-626 (2023) - [c240]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. AAAI 2023: 7503-7511 - [c239]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
Approximating Two-Layer Feedforward Networks for Efficient Transformers. EMNLP (Findings) 2023: 674-692 - [c238]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. EMNLP 2023: 9455-9465 - [c237]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. ICCV 2023: 1955-1965 - [c236]Kazuki Irie, Jürgen Schmidhuber:
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. ICLR 2023 - [c235]Kenny John Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. ICML 2023: 40254-40276 - [c234]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks. IWAI 2023: 254-274 - [c233]Aleksandar Stanic, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber:
Contrastive Training of Complex-Valued Autoencoders for Object Discovery. NeurIPS 2023 - [i150]Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, Mohamed Elhoseiny:
Guiding Online Reinforcement Learning with Action-Free Offline Pretraining. CoRR abs/2301.12876 (2023) - [i149]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Topological Neural Discrete Representation Learning à la Kohonen. CoRR abs/2302.07950 (2023) - [i148]Kazuki Irie, Jürgen Schmidhuber:
Accelerating Neural Self-Improvement via Bootstrapping. CoRR abs/2305.01547 (2023) - [i147]Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, Xian Li:
Large Language Model Programs. CoRR abs/2305.05364 (2023) - [i146]Aleksandar Stanic, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber:
Contrastive Training of Complex-Valued Autoencoders for Object Discovery. CoRR abs/2305.15001 (2023) - [i145]Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya A. Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber:
Mindstorms in Natural Language-Based Societies of Mind. CoRR abs/2305.17066 (2023) - [i144]Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber:
Exploring the Promise and Limits of Real-Time Recurrent Learning. CoRR abs/2305.19044 (2023) - [i143]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. CoRR abs/2308.07795 (2023) - [i142]Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag:
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR abs/2309.11197 (2023) - [i141]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
Approximating Two-Layer Feedforward Networks for Efficient Transformers. CoRR abs/2310.10837 (2023) - [i140]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. CoRR abs/2310.16076 (2023) - [i139]Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, Anand Gopalakrishnan:
Unsupervised Musical Object Discovery from Audio. CoRR abs/2311.07534 (2023) - [i138]Lukas Tuggener, Thilo Stadelmann, Jürgen Schmidhuber:
Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation. CoRR abs/2311.08525 (2023) - [i137]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Automating Continual Learning. CoRR abs/2312.00276 (2023) - [i136]Róbert Csordás, Piotr Piekos, Kazuki Irie, Jürgen Schmidhuber:
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention. CoRR abs/2312.07987 (2023) - 2022
- [j75]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it enough to optimize CNN architectures on ImageNet? Frontiers Comput. Sci. 4 (2022) - [j74]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston:
Bayesian Brains and the Rényi Divergence. Neural Comput. 34(4): 829-855 (2022) - [j73]Aditya A. Ramesh, Paulo E. Rauber, Michelangelo Conserva, Jürgen Schmidhuber:
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits. Neural Comput. 34(11): 2232-2272 (2022) - [j72]Michael Wand, Morten Bak Kristoffersen, Andreas W. Franzke, Jürgen Schmidhuber:
Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control. IEEE Trans. Biomed. Eng. 69(7): 2283-2293 (2022) - [c232]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. AAAI 2022: 8361-8369 - [c231]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations. EMNLP 2022: 9758-9767 - [c230]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. ICLR 2022 - [c229]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. ICML 2022: 9639-9659 - [c228]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. ICML 2022: 9660-9677 - [c227]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. NeurIPS 2022 - [c226]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. NeurIPS 2022 - [i135]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. CoRR abs/2202.05780 (2022) - [i134]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. CoRR abs/2202.05798 (2022) - [i133]Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL. CoRR abs/2202.11960 (2022) - [i132]Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR abs/2202.12742 (2022) - [i131]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers. CoRR abs/2203.13573 (2022) - [i130]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets. CoRR abs/2205.06595 (2022) - [i129]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. CoRR abs/2206.01649 (2022) - [i128]Francesco Faccio, Aditya A. Ramesh, Vincent Herrmann, Jean Harb, Jürgen Schmidhuber:
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States. CoRR abs/2207.01566 (2022) - [i127]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. CoRR abs/2207.01570 (2022) - [i126]Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber:
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter. CoRR abs/2208.03374 (2022) - [i125]Kazuki Irie, Jürgen Schmidhuber:
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. CoRR abs/2210.06184 (2022) - [i124]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations. CoRR abs/2210.06350 (2022) - [i123]Kenny Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. CoRR abs/2211.02222 (2022) - [i122]Kazuki Irie, Jürgen Schmidhuber:
Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks. CoRR abs/2211.09440 (2022) - [i121]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. CoRR abs/2211.10282 (2022) - [i120]Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber:
On Narrative Information and the Distillation of Stories. CoRR abs/2211.12423 (2022) - [i119]Jürgen Schmidhuber:
Annotated History of Modern AI and Deep Learning. CoRR abs/2212.11279 (2022) - [i118]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks. CoRR abs/2212.14374 (2022) - [i117]Louis Kirsch, Jürgen Schmidhuber:
Eliminating Meta Optimization Through Self-Referential Meta Learning. CoRR abs/2212.14392 (2022) - 2021
- [j71]Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients. Neural Comput. 33(6): 1498-1553 (2021) - [j70]Ariel Ruiz-Garcia, Jürgen Schmidhuber, Vasile Palade, Clive Cheong Took, Danilo P. Mandic:
Deep neural network representation and Generative Adversarial Learning. Neural Networks 139: 199-200 (2021) - [c225]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. AAAI 2021: 9730-9738 - [c224]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. EMNLP (1) 2021: 619-634 - [c223]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. ICLR 2021 - [c222]Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber:
Parameter-Based Value Functions. ICLR 2021 - [c221]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. ICLR 2021 - [c220]Ðorðe Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. ICLR 2021 - [c219]Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber:
Learning Associative Inference Using Fast Weight Memory. ICLR 2021 - [c218]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021: 9355-9366 - [c217]Krsto Prorokovic, Michael Wand, Jürgen Schmidhuber:
Improving Stateful Premise Selection with Transformers. CICM 2021: 84-89 - [c216]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. NeurIPS 2021: 7703-7717 - [c215]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. NeurIPS 2021: 14122-14134 - [i116]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Memory Systems. CoRR abs/2102.11174 (2021) - [i115]Djordje Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. CoRR abs/2103.08877 (2021) - [i114]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it Enough to Optimize CNN Architectures on ImageNet? CoRR abs/2103.09108 (2021) - [i113]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. CoRR abs/2106.06295 (2021) - [i112]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston:
Bayesian brains and the Rényi divergence. CoRR abs/2107.05438 (2021) - [i111]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. CoRR abs/2107.09088 (2021) - [i110]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. CoRR abs/2108.12284 (2021) - [i109]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. CoRR abs/2110.07732 (2021) - [i108]Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber:
Automatic Embedding of Stories Into Collections of Independent Media. CoRR abs/2111.02216 (2021) - [i107]Kazuki Irie, Jürgen Schmidhuber:
Training and Generating Neural Networks in Compressed Weight Space. CoRR abs/2112.15545 (2021) - [i106]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Improving Baselines in the Wild. CoRR abs/2112.15550 (2021) - 2020
- [j69]Jürgen Schmidhuber:
Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991). Neural Networks 127: 58-66 (2020) - [j68]Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly:
Investigating object compositionality in Generative Adversarial Networks. Neural Networks 130: 309-325 (2020) - [c214]Matteo Riva, Michael Wand, Jürgen Schmidhuber:
Motion Dynamics Improve Speaker-Independent Lipreading. ICASSP 2020: 4407-4411 - [c213]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR 2020 - [c212]Lukas Tuggener, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, Thilo Stadelmann:
The DeepScoresV2 Dataset and Benchmark for Music Object Detection. ICPR 2020: 9188-9195 - [c211]Michael Wand, Jürgen Schmidhuber:
Fusion Architectures for Word-Based Audiovisual Speech Recognition. INTERSPEECH 2020: 3491-3495 - [i105]Jürgen Schmidhuber:
Deep Learning: Our Miraculous Year 1990-1991. CoRR abs/2005.05744 (2020) - [i104]Francesco Faccio, Jürgen Schmidhuber:
Parameter-based Value Functions. CoRR abs/2006.09226 (2020) - [i103]Aditya A. Ramesh, Paulo E. Rauber, Jürgen Schmidhuber:
Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits. CoRR abs/2007.04750 (2020) - [i102]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. CoRR abs/2010.02066 (2020) - [i101]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. CoRR abs/2010.03635 (2020) - [i100]Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber:
Learning Associative Inference Using Fast Weight Memory. CoRR abs/2011.07831 (2020) - [i99]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. CoRR abs/2011.12930 (2020) - [i98]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
On the Binding Problem in Artificial Neural Networks. CoRR abs/2012.05208 (2020) - [i97]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. CoRR abs/2012.14905 (2020)
2010 – 2019
- 2019
- [c210]Krsto Prorokovic, Michael Wand, Tanja Schultz, Jürgen Schmidhuber:
Adaptation of an EMG-Based Speech Recognizer via Meta-Learning. GlobalSIP 2019: 1-5 - [c209]Róbert Csordás, Jürgen Schmidhuber:
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. ICLR (Poster) 2019 - [c208]Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. ICLR (Poster) 2019 - [c207]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019: 14222-14235 - [i96]Róbert Csordás, Jürgen Schmidhuber:
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. CoRR abs/1904.10278 (2019) - [i95]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR abs/1905.12506 (2019) - [i94]Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber:
A Perspective on Objects and Systematic Generalization in Model-Based RL. CoRR abs/1906.01035 (2019) - [i93]Jürgen Schmidhuber:
Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization. CoRR abs/1906.04493 (2019) - [i92]Timon Willi, Jonathan Masci, Jürgen Schmidhuber, Christian Osendorfer:
Recurrent Neural Processes. CoRR abs/1906.05915 (2019) - [i91]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. CoRR abs/1910.04098 (2019) - [i90]Aleksandar Stanic, Jürgen Schmidhuber:
R-SQAIR: Relational Sequential Attend, Infer, Repeat. CoRR abs/1910.05231 (2019) - [i89]Imanol Schlag, Paul Smolensky, Roland Fernandez, Nebojsa Jojic, Jürgen Schmidhuber, Jianfeng Gao:
Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving. CoRR abs/1910.06611 (2019) - [i88]Jürgen Schmidhuber:
Reinforcement Learning Upside Down: Don't Predict Rewards - Just Map Them to Actions. CoRR abs/1912.02875 (2019) - [i87]Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaskowski, Jürgen Schmidhuber:
Training Agents using Upside-Down Reinforcement Learning. CoRR abs/1912.02877 (2019) - 2018
- [c206]Michael Wand, Jürgen Schmidhuber, Ngoc Thang Vu:
Investigations on End- to-End Audiovisual Fusion. ICASSP 2018: 3041-3045 - [c205]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. ICLR (Poster) 2018 - [c204]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects. ICPR 2018: 3704-3709 - [c203]Michael Wand, Tanja Schultz, Jürgen Schmidhuber:
Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. INTERSPEECH 2018: 3167-3171 - [c202]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. ISMIR 2018: 271-278 - [c201]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. NeurIPS 2018: 2455-2467 - [c200]Imanol Schlag, Jürgen Schmidhuber:
Learning to Reason with Third Order Tensor Products. NeurIPS 2018: 10003-10014 - [i86]Jürgen Schmidhuber:
One Big Net For Everything. CoRR abs/1802.08864 (2018) - [i85]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. CoRR abs/1802.10353 (2018) - [i84]David Ha, Jürgen Schmidhuber:
World Models. CoRR abs/1803.10122 (2018) - [i83]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects. CoRR abs/1804.00525 (2018) - [i82]Michael Wand, Ngoc Thang Vu, Jürgen Schmidhuber:
Investigations on End-to-End Audiovisual Fusion. CoRR abs/1804.11127 (2018) - [i81]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. CoRR abs/1805.10548 (2018) - [i80]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. CoRR abs/1809.01999 (2018) - [i79]Imanol Schlag, Jürgen Schmidhuber:
Learning to Reason with Third-Order Tensor Products. CoRR abs/1811.12143 (2018) - 2017
- [j67]Varun Raj Kompella, Marijn F. Stollenga, Matthew D. Luciw, Jürgen Schmidhuber:
Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. Artif. Intell. 247: 313-335 (2017) - [j66]Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber:
LSTM: A Search Space Odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10): 2222-2232 (2017) - [c199]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. ICLR (Poster) 2017 - [c198]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. ICLR (Workshop) 2017 - [c197]Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. ICML 2017: 4189-4198 - [c196]Michael Wand, Jürgen Schmidhuber:
Improving Speaker-Independent Lipreading with Domain-Adversarial Training. INTERSPEECH 2017: 3662-3666 - [c195]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. NIPS 2017: 6691-6701 - [c194]Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber:
The Sacred Infrastructure for Computational Research. SciPy 2017: 49-56 - [r1]Jürgen Schmidhuber:
Deep Learning. Encyclopedia of Machine Learning and Data Mining 2017: 338-348 - [i78]Michael Wand, Jürgen Schmidhuber:
Improving Speaker-Independent Lipreading with Domain-Adversarial Training. CoRR abs/1708.01565 (2017) - [i77]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. CoRR abs/1708.03498 (2017) - [i76]Paulo E. Rauber, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. CoRR abs/1711.06006 (2017) - 2016
- [j65]Varun Raj Kompella, Matthew D. Luciw, Marijn F. Stollenga, Jürgen Schmidhuber:
Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis. Neural Comput. 28(8): 1599-1662 (2016) - [j64]Alessandro Giusti, Jerome Guzzi, Dan C. Ciresan, Fang-Lin He, Juan P. Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jürgen Schmidhuber, Gianni Di Caro, Davide Scaramuzza, Luca Maria Gambardella:
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots. IEEE Robotics Autom. Lett. 1(2): 661-667 (2016) - [c193]Bas R. Steunebrink, Kristinn R. Thórisson, Jürgen Schmidhuber:
Growing Recursive Self-Improvers. AGI 2016: 129-139 - [c192]Sjoerd van Steenkiste, Jan Koutník, Kurt Driessens, Jürgen Schmidhuber:
A Wavelet-based Encoding for Neuroevolution. GECCO 2016: 517-524 - [c191]Michael Wand, Jan Koutník, Jürgen Schmidhuber:
Lipreading with long short-term memory. ICASSP 2016: 6115-6119 - [c190]Michael Wand, Jürgen Schmidhuber:
Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition. INTERSPEECH 2016: 3032-3036 - [c189]Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Harri Valpola, Jürgen Schmidhuber:
Tagger: Deep Unsupervised Perceptual Grouping. NIPS 2016: 4484-4492 - [i75]Michael Wand, Jan Koutník, Jürgen Schmidhuber:
Lipreading with Long Short-Term Memory. CoRR abs/1601.08188 (2016) - [i74]Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, Harri Valpola:
Tagger: Deep Unsupervised Perceptual Grouping. CoRR abs/1606.06724 (2016) - [i73]Simon Harding, Jan Koutník, Klaus Greff, Jürgen Schmidhuber, Andy Adamatzky:
Discovering Boolean Gates in Slime Mould. CoRR abs/1607.02168 (2016) - [i72]Julian G. Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. CoRR abs/1607.03474 (2016) - [i71]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. CoRR abs/1612.07771 (2016) - 2015
- [j63]Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio A. González, Anders Boesen Lindbo Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Ciresan, Jürgen Schmidhuber, Alessandro Giusti, Luca Maria Gambardella, Faik Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frédéric Precioso, Violet Snell, Josef Kittler, Teófilo Emídio de Campos, Adnan Mujahid Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim:
Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Anal. 20(1): 237-248 (2015) - [j62]Jürgen Schmidhuber:
Deep learning in neural networks: An overview. Neural Networks 61: 85-117 (2015) - [j61]Jürgen Schmidhuber:
Deep Learning. Scholarpedia 10(11): 32832 (2015) - [c188]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Jürgen Schmidhuber:
Anytime Bounded Rationality. AGI 2015: 121-130 - [c187]Marijn F. Stollenga, Alan J. Lockett, Jürgen Schmidhuber:
The Natural Gradient as a control signal for a humanoid robot. Humanoids 2015: 187-193 - [c186]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. NIPS 2015: 2377-2385 - [c185]Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber:
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. NIPS 2015: 2998-3006 - [c184]Rupesh Kumar Srivastava, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Understanding Locally Competitive Networks. ICLR (Poster) 2015 - [i70]Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber:
LSTM: A Search Space Odyssey. CoRR abs/1503.04069 (2015) - [i69]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Highway Networks. CoRR abs/1505.00387 (2015) - [i68]Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber:
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. CoRR abs/1506.07452 (2015) - [i67]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. CoRR abs/1507.06228 (2015) - [i66]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Binding via Reconstruction Clustering. CoRR abs/1511.06418 (2015) - [i65]Jürgen Schmidhuber:
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. CoRR abs/1511.09249 (2015) - 2014
- [j60]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [j59]Jonathan Masci, Michael M. Bronstein, Alexander M. Bronstein, Jürgen Schmidhuber:
Multimodal Similarity-Preserving Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 36(4): 824-830 (2014) - [j58]Hung Quoc Ngo, Matthew D. Luciw, Jawad Nagi, Alexander Förster, Jürgen Schmidhuber, Ngo Anh Vien:
Efficient Interactive Multiclass Learning from Binary Feedback. ACM Trans. Interact. Intell. Syst. 4(3): 12:1-12:25 (2014) - [c183]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Haris Dindo, Giovanni Pezzulo, Manuel Rodríguez, Carlos Hernández, Dimitri Ognibene, Jürgen Schmidhuber, Ricardo Sanz, Helgi Páll Helgason, Antonio Chella:
Bounded Seed-AGI. AGI 2014: 85-96 - [c182]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. GECCO 2014: 541-548 - [c181]Jawad Nagi, Hung Quoc Ngo, Jürgen Schmidhuber, Luca Maria Gambardella, Gianni A. Di Caro:
Human-robot cooperation: fast, interactive learning from binary feedback. HRI 2014: 107 - [c180]Matthew D. Luciw, Yulia Sandamirskaya, Sohrob Kazerounian, Jürgen Schmidhuber, Gregor Schöner:
Reinforcement and shaping in learning action sequences with neural dynamics. ICDL-EPIROB 2014: 48-55 - [c179]Jürgen Leitner, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Reactive Reaching and Grasping on a Humanoid - Towards Closing the Action-Perception Loop on the iCub. ICINCO (1) 2014: 102-109 - [c178]Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. ICML 2014: 1863-1871 - [c177]Varun Raj Kompella, Marijn F. Stollenga, Matthew D. Luciw, Jürgen Schmidhuber:
Explore to see, learn to perceive, get the actions for free: SKILLABILITY. IJCNN 2014: 2705-2712 - [c176]Jürgen Leitner, Alexander Förster, Jürgen Schmidhuber:
Improving robot vision models for object detection through interaction. IJCNN 2014: 3355-3362 - [c175]Alessandro Giusti, Claudio Caccia, Dan C. Ciresan, Jürgen Schmidhuber, Luca Maria Gambardella:
A comparison of algorithms and humans for mitosis detection. ISBI 2014: 1360-1363 - [c174]Jan Funke, Julien N. P. Martel, Stephan Gerhard, Björn Andres, Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber, Hanspeter Pfister, Albert Cardona, Matthew Cook:
Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes. MICCAI (1) 2014: 17-24 - [c173]Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Deep Networks with Internal Selective Attention through Feedback Connections. NIPS 2014: 3545-3553 - [c172]Varun Raj Kompella, Sohrob Kazerounian, Jürgen Schmidhuber:
An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning. SAB 2014: 176-187 - [c171]Matthew D. Luciw, Sohrob Kazerounian, Yulia Sandamirskaya, Gregor Schöner, Jürgen Schmidhuber:
Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics. SAB 2014: 198-209 - [c170]Marijn F. Stollenga, Jürgen Schmidhuber, Faustino J. Gomez:
Rapid Humanoid Motion Learning through Coordinated, Parallel Evolution. SAB 2014: 210-219 - [c169]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
Online Evolution of Deep Convolutional Network for Vision-Based Reinforcement Learning. SAB 2014: 260-269 - [p7]Jonathan Masci, Davide Migliore, Michael M. Bronstein, Jürgen Schmidhuber:
Descriptor Learning for Omnidirectional Image Matching. Registration and Recognition in Images and Videos 2014: 49-62 - [i64]Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. CoRR abs/1402.3511 (2014) - [i63]Jürgen Schmidhuber:
Deep Learning in Neural Networks: An Overview. CoRR abs/1404.7828 (2014) - [i62]Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Deep Networks with Internal Selective Attention through Feedback Connections. CoRR abs/1407.3068 (2014) - [i61]Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio A. González, Anders Boesen Lindbo Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Ciresan, Jürgen Schmidhuber, Alessandro Giusti, Luca Maria Gambardella, Faik Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frédéric Precioso, Violet Snell, Josef Kittler, Teófilo Emídio de Campos, Adnan Mujahid Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim:
Assessment of algorithms for mitosis detection in breast cancer histopathology images. CoRR abs/1411.5825 (2014) - 2013
- [j57]Matthew D. Luciw, Varun Raj Kompella, Sohrob Kazerounian, Jürgen Schmidhuber:
An intrinsic value system for developing multiple invariant representations with incremental slowness learning. Frontiers Neurorobotics 7: 9 (2013) - [j56]Mikhail Frank, Jürgen Leitner, Marijn F. Stollenga, Alexander Förster, Jürgen Schmidhuber:
Curiosity driven reinforcement learning for motion planning on humanoids. Frontiers Neurorobotics 7: 25 (2013) - [j55]Ulrich Rührmair, Jan Sölter, Frank Sehnke, Xiaolin Xu, Ahmed Mahmoud, Vera Stoyanova, Gideon Dror, Jürgen Schmidhuber, Wayne P. Burleson, Srinivas Devadas:
PUF Modeling Attacks on Simulated and Silicon Data. IEEE Trans. Inf. Forensics Secur. 8(11): 1876-1891 (2013) - [c168]Bas R. Steunebrink, Jan Koutník, Kristinn R. Thórisson, Eric Nivel, Jürgen Schmidhuber:
Resource-Bounded Machines are Motivated to be Effective, Efficient, and Curious. AGI 2013: 119-129 - [c167]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Humanoid learns to detect its own hands. IEEE Congress on Evolutionary Computation 2013: 1411-1418 - [c166]Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving large-scale neural networks for vision-based TORCS. FDG 2013: 206-212 - [c165]Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber:
A linear time natural evolution strategy for non-separable functions. GECCO (Companion) 2013: 61-62 - [c164]Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving large-scale neural networks for vision-based reinforcement learning. GECCO 2013: 1061-1068 - [c163]Jonathan Masci, Alessandro Giusti, Dan C. Ciresan, Gabriel Fricout, Jürgen Schmidhuber:
A fast learning algorithm for image segmentation with max-pooling convolutional networks. ICIP 2013: 2713-2717 - [c162]Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
Fast image scanning with deep max-pooling convolutional neural networks. ICIP 2013: 4034-4038 - [c161]Hung Quoc Ngo, Matthew David Luciw, Ngo Anh Vien, Jürgen Schmidhuber:
Upper Confidence Weighted Learning for Efficient Exploration in Multiclass Prediction with Binary Feedback. IJCAI 2013: 2488-2494 - [c160]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots. IJCNN 2013: 1-7 - [c159]Jonathan Masci, Ueli Meier, Gabriel Fricout, Jürgen Schmidhuber:
Multi-scale pyramidal pooling network for generic steel defect classification. IJCNN 2013: 1-8 - [c158]Marijn F. Stollenga, Leo Pape, Mikhail Frank, Jürgen Leitner, Alexander Förster, Jürgen Schmidhuber:
Task-relevant roadmaps: A framework for humanoid motion planning. IROS 2013: 5772-5778 - [c157]Jonathan Masci, Jesús Angulo, Jürgen Schmidhuber:
A Learning Framework for Morphological Operators Using Counter-Harmonic Mean. ISMM 2013: 329-340 - [c156]Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber:
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. MICCAI (2) 2013: 411-418 - [c155]Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino J. Gomez, Jürgen Schmidhuber:
Compete to Compute. NIPS 2013: 2310-2318 - [p6]Jürgen Schmidhuber:
Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity. Intrinsically Motivated Learning in Natural and Artificial Systems 2013: 95-128 - [i60]Jonathan Masci, Alessandro Giusti, Dan C. Ciresan, Gabriel Fricout, Jürgen Schmidhuber:
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks. CoRR abs/1302.1690 (2013) - [i59]Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks. CoRR abs/1302.1700 (2013) - [i58]Somayeh Danafar, Paola M. V. Rancoita, Tobias Glasmachers, Kevin Whittingstall, Jürgen Schmidhuber:
Testing Hypotheses by Regularized Maximum Mean Discrepancy. CoRR abs/1305.0423 (2013) - [i57]Dan C. Ciresan, Jürgen Schmidhuber:
Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification. CoRR abs/1309.0261 (2013) - [i56]Jürgen Schmidhuber:
My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013. CoRR abs/1312.5548 (2013) - [i55]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Haris Dindo, Giovanni Pezzulo, Manuel Rodríguez, Carlos Hernández, Dimitri Ognibene, Jürgen Schmidhuber, Ricardo Sanz, Helgi Páll Helgason, Antonio Chella, Gudberg K. Jonsson:
Bounded Recursive Self-Improvement. CoRR abs/1312.6764 (2013) - [i54]Ulrich Rührmair, Jan Sölter, Frank Sehnke, Xiaolin Xu, Ahmed Mahmoud, Vera Stoyanova, Gideon Dror, Jürgen Schmidhuber, Wayne P. Burleson, Srinivas Devadas:
PUF Modeling Attacks on Simulated and Silicon Data. IACR Cryptol. ePrint Arch. 2013: 112 (2013) - 2012
- [j54]Somayeh Danafar, Alessandro Giusti, Jürgen Schmidhuber:
Correction: novel Kernel-based recognizers of human actions. EURASIP J. Adv. Signal Process. 2012: 124 (2012) - [j53]Tobias Glasmachers, Jan Koutník, Jürgen Schmidhuber:
Kernel representations for evolving continuous functions. Evol. Intell. 5(3): 171-187 (2012) - [j52]Leo Pape, Calogero M. Oddo, Marco Controzzi, Christian Cipriani, Alexander Förster, Maria Chiara Carrozza, Jürgen Schmidhuber:
Learning tactile skills through curious exploration. Frontiers Neurorobotics 6: 6 (2012) - [j51]Jürgen Schmidhuber:
Turing: Keep his work in perspective. Nat. 483(7391): 541 (2012) - [j50]Varun Raj Kompella, Matthew D. Luciw, Jürgen Schmidhuber:
Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams. Neural Comput. 24(11): 2994-3024 (2012) - [j49]Dan C. Ciresan, Ueli Meier, Jonathan Masci, Jürgen Schmidhuber:
Multi-column deep neural network for traffic sign classification. Neural Networks 32: 333-338 (2012) - [c154]Linus Gisslén, Mark B. Ring, Matthew D. Luciw, Jürgen Schmidhuber:
Modular Value Iteration through Regional Decomposition. AGI 2012: 69-78 - [c153]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
An Integrated, Modular Framework for Computer Vision and Cognitive Robotics Research (icVision). BICA 2012: 205-210 - [c152]Dan C. Ciresan, Ueli Meier, Jürgen Schmidhuber:
Multi-column deep neural networks for image classification. CVPR 2012: 3642-3649 - [c151]Rupesh Kumar Srivastava, Jürgen Schmidhuber, Faustino J. Gomez:
Generalized compressed network search. GECCO (Companion) 2012: 647-648 - [c150]Simon Harding, Vincent Graziano, Jürgen Leitner, Jürgen Schmidhuber:
MT-CGP: mixed type cartesian genetic programming. GECCO 2012: 751-758 - [c149]Faustino J. Gomez, Jan Koutník, Jürgen Schmidhuber:
Complexity search for compressed neural networks. GECCO (Companion) 2012: 1455-1456 - [c148]Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Reflexive Collision Response with Virtual Skin - Roadmap Planning Meets Reinforcement Learning. ICAART (1) 2012: 642-651 - [c147]Matthew D. Luciw, Jürgen Schmidhuber:
Low Complexity Proto-Value Function Learning from Sensory Observations with Incremental Slow Feature Analysis. ICANN (2) 2012: 279-287 - [c146]Sohrob Kazerounian, Matthew D. Luciw, Yulia Sandamirskaya, Mathis Richter, Jürgen Schmidhuber, Gregor Schöner:
Autonomous reinforcement of behavioral sequences in neural dynamics. ICDL-EPIROB 2012: 1-2 - [c145]Varun Raj Kompella, Matthew D. Luciw, Marijn F. Stollenga, Leo Pape, Jürgen Schmidhuber:
Autonomous learning of abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis. ICDL-EPIROB 2012: 1-8 - [c144]Jürgen Leitner, Pramod Chandrashekhariah, Simon Harding, Mikhail Frank, Gabriele Spina, Alexander Förster, Jochen Triesch, Jürgen Schmidhuber:
Autonomous learning of robust visual object detection and identification on a humanoid. ICDL-EPIROB 2012: 1-6 - [c143]Rupesh Kumar Srivastava, Bas R. Steunebrink, Marijn F. Stollenga, Jürgen Schmidhuber:
Continually adding self-invented problems to the repertoire: First experiments with POWERPLAY. ICDL-EPIROB 2012: 1-6 - [c142]Mikhail Frank, Jürgen Leitner, Marijn F. Stollenga, Simon Harding, Alexander Förster, Jürgen Schmidhuber:
The Modular Behavioral Environment for Humanoids and other Robots (MoBeE). ICINCO (2) 2012: 304-313 - [c141]Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
On the Size of the Online Kernel Sparsification Dictionary. ICML 2012 - [c140]Dan Claudiu Ciresan, Ueli Meier, Jürgen Schmidhuber:
Transfer learning for Latin and Chinese characters with Deep Neural Networks. IJCNN 2012: 1-6 - [c139]Jonathan Masci, Ueli Meier, Dan C. Ciresan, Jürgen Schmidhuber, Gabriel Fricout:
Steel defect classification with Max-Pooling Convolutional Neural Networks. IJCNN 2012: 1-6 - [c138]Hung Quoc Ngo, Matthew D. Luciw, Alexander Förster, Jürgen Schmidhuber:
Learning skills from play: Artificial curiosity on a Katana robot arm. IJCNN 2012: 1-8 - [c137]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Transferring spatial perception between robots operating in a shared workspace. IROS 2012: 1507-1512 - [c136]Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. NIPS 2012: 2852-2860 - [c135]Faustino J. Gomez, Jan Koutník, Jürgen Schmidhuber:
Compressed Network Complexity Search. PPSN (1) 2012: 316-326 - [c134]Rupesh Kumar Srivastava, Jürgen Schmidhuber, Faustino J. Gomez:
Generalized Compressed Network Search. PPSN (1) 2012: 337-346 - [c133]Jawad Nagi, Hung Quoc Ngo, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber, Gianni A. Di Caro:
Incremental learning using partial feedback for gesture-based human-swarm interaction. RO-MAN 2012: 898-905 - [p5]Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep Big Multilayer Perceptrons for Digit Recognition. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 581-598 - [i53]Vincent Graziano, Faustino J. Gomez, Mark B. Ring, Jürgen Schmidhuber:
T-Learning. CoRR abs/1201.0292 (2012) - [i52]Dan C. Ciresan, Ueli Meier, Jürgen Schmidhuber:
Multi-column Deep Neural Networks for Image Classification. CoRR abs/1202.2745 (2012) - [i51]Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
On the Size of the Online Kernel Sparsification Dictionary. CoRR abs/1206.4623 (2012) - [i50]Jonathan Masci, Michael M. Bronstein, Alexander M. Bronstein, Jürgen Schmidhuber:
Multimodal similarity-preserving hashing. CoRR abs/1207.1522 (2012) - [i49]Jonathan Masci, Ueli Meier, Gabriel Fricout, Jürgen Schmidhuber:
Object Recognition with Multi-Scale Pyramidal Pooling Networks. CoRR abs/1207.1765 (2012) - [i48]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient Natural Evolution Strategies. CoRR abs/1209.5853 (2012) - [i47]Jürgen Schmidhuber:
Self-Delimiting Neural Networks. CoRR abs/1210.0118 (2012) - [i46]Rupesh Kumar Srivastava, Bas R. Steunebrink, Jürgen Schmidhuber:
First Experiments with PowerPlay. CoRR abs/1210.8385 (2012) - [i45]Jonathan Masci, Jesús Angulo, Jürgen Schmidhuber:
A Learning Framework for Morphological Operators using Counter-Harmonic Mean. CoRR abs/1212.2546 (2012) - [i44]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
A Frequency-Domain Encoding for Neuroevolution. CoRR abs/1212.6521 (2012) - 2011
- [j48]Matteo Gagliolo, Jürgen Schmidhuber:
Algorithm portfolio selection as a bandit problem with unbounded losses. Ann. Math. Artif. Intell. 61(2): 49-86 (2011) - [c132]Jürgen Schmidhuber:
Lifelong Credit Assignment with the Success-Story Algorithm. Lifelong Learning 2011 - [c131]Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber:
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. AGI 2011: 21-30 - [c130]Linus Gisslén, Matthew D. Luciw, Vincent Graziano, Jürgen Schmidhuber:
Sequential Constant Size Compressors for Reinforcement Learning. AGI 2011: 31-40 - [c129]Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments. AGI 2011: 41-51 - [c128]Tobias Glasmachers, Jürgen Schmidhuber:
Optimal Direct Policy Search. AGI 2011: 52-61 - [c127]Jürgen Schmidhuber, Dan C. Ciresan, Ueli Meier, Jonathan Masci, Alex Graves:
On Fast Deep Nets for AGI Vision. AGI 2011: 243-246 - [c126]Bas R. Steunebrink, Jürgen Schmidhuber:
A Family of Gödel Machine Implementations. AGI 2011: 275-280 - [c125]Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber:
Curiosity-driven optimization. IEEE Congress on Evolutionary Computation 2011: 1343-1349 - [c124]Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber:
High dimensions and heavy tails for natural evolution strategies. GECCO 2011: 845-852 - [c123]Varun Raj Kompella, Leo Pape, Jonathan Masci, Mikhail Frank, Jürgen Schmidhuber:
AutoIncSFA and vision-based developmental learning for humanoid robots. Humanoids 2011: 622-629 - [c122]Jonathan Masci, Ueli Meier, Dan C. Ciresan, Jürgen Schmidhuber:
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction. ICANN (1) 2011: 52-59 - [c121]Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Convolutional Neural Network Committees for Handwritten Character Classification. ICDAR 2011: 1135-1139 - [c120]Ueli Meier, Dan Claudiu Ciresan, Luca Maria Gambardella, Jürgen Schmidhuber:
Better Digit Recognition with a Committee of Simple Neural Nets. ICDAR 2011: 1250-1254 - [c119]Giuseppe Cuccu, Matthew D. Luciw, Jürgen Schmidhuber, Faustino J. Gomez:
Intrinsically motivated neuroevolution for vision-based reinforcement learning. ICDL-EPIROB 2011: 1-7 - [c118]Matthew D. Luciw, Vincent Graziano, Mark B. Ring, Jürgen Schmidhuber:
Artificial curiosity with planning for autonomous perceptual and cognitive development. ICDL-EPIROB 2011: 1-8 - [c117]Mark B. Ring, Tom Schaul, Jürgen Schmidhuber:
The two-dimensional organization of behavior. ICDL-EPIROB 2011: 1-8 - [c116]Yi Sun, Faustino J. Gomez, Mark B. Ring, Jürgen Schmidhuber:
Incremental Basis Construction from Temporal Difference Error. ICML 2011: 481-488 - [c115]Jawad Nagi, Frederick Ducatelle, Gianni A. Di Caro, Dan C. Ciresan, Ueli Meier, Alessandro Giusti, Farrukh Nagi, Jürgen Schmidhuber, Luca Maria Gambardella:
Max-pooling convolutional neural networks for vision-based hand gesture recognition. ICSIPA 2011: 342-347 - [c114]Dan Claudiu Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
Flexible, High Performance Convolutional Neural Networks for Image Classification. IJCAI 2011: 1237-1242 - [c113]Varun Raj Kompella, Matthew D. Luciw, Jürgen Schmidhuber:
Incremental Slow Feature Analysis. IJCAI 2011: 1354-1359 - [c112]Leo Pape, Faustino J. Gomez, Juergen Ring, Jürgen Schmidhuber:
Modular deep belief networks that do not forget. IJCNN 2011: 1191-1198 - [c111]Dan C. Ciresan, Ueli Meier, Jonathan Masci, Jürgen Schmidhuber:
A committee of neural networks for traffic sign classification. IJCNN 2011: 1918-1921 - [c110]Vincent Graziano, Jan Koutník, Jürgen Schmidhuber:
Unsupervised Modeling of Partially Observable Environments. ECML/PKDD (1) 2011: 503-515 - [e1]Jürgen Schmidhuber, Kristinn R. Thórisson, Moshe Looks:
Artificial General Intelligence - 4th International Conference, AGI 2011, Mountain View, CA, USA, August 3-6, 2011. Proceedings. Lecture Notes in Computer Science 6830, Springer 2011, ISBN 978-3-642-22886-5 [contents] - [i43]Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
High-Performance Neural Networks for Visual Object Classification. CoRR abs/1102.0183 (2011) - [i42]Dan C. Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Handwritten Digit Recognition with a Committee of Deep Neural Nets on GPUs. CoRR abs/1103.4487 (2011) - [i41]Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments. CoRR abs/1103.5708 (2011) - [i40]Yi Sun, Faustino J. Gomez, Tom Schaul, Jürgen Schmidhuber:
A Linear Time Natural Evolution Strategy for Non-Separable Functions. CoRR abs/1106.1998 (2011) - [i39]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber:
Natural Evolution Strategies. CoRR abs/1106.4487 (2011) - [i38]Tom Schaul, Julian Togelius, Jürgen Schmidhuber:
Measuring Intelligence through Games. CoRR abs/1109.1314 (2011) - [i37]Varun Raj Kompella, Matthew D. Luciw, Jürgen Schmidhuber:
Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams. CoRR abs/1112.2113 (2011) - [i36]Jürgen Schmidhuber:
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem. CoRR abs/1112.5309 (2011) - [i35]Jonathan Masci, Davide Migliore, Michael M. Bronstein, Jürgen Schmidhuber:
Descriptor learning for omnidirectional image matching. CoRR abs/1112.6291 (2011) - 2010
- [j47]Somayeh Danafar, Alessandro Giusti, Jürgen Schmidhuber:
Novel Kernel-Based Recognizers of Human Actions. EURASIP J. Adv. Signal Process. 2010 (2010) - [j46]Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber:
Recurrent policy gradients. Log. J. IGPL 18(5): 620-634 (2010) - [j45]Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber:
PyBrain. J. Mach. Learn. Res. 11: 743-746 (2010) - [j44]Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep, Big, Simple Neural Nets for Handwritten Digit Recognition. Neural Comput. 22(12): 3207-3220 (2010) - [j43]Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, Jürgen Schmidhuber:
Parameter-exploring policy gradients. Neural Networks 23(4): 551-559 (2010) - [j42]Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber:
Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics 1(1): 14-24 (2010) - [j41]Tom Schaul, Jürgen Schmidhuber:
Metalearning. Scholarpedia 5(6): 4650 (2010) - [j40]Jürgen Schmidhuber:
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990-2010). IEEE Trans. Auton. Ment. Dev. 2(3): 230-247 (2010) - [c109]Giorgio Corani, Alessandro Giusti, Davide Migliore, Jürgen Schmidhuber:
Robust Texture Recognition Using Credal Classifiers. BMVC 2010: 1-10 - [c108]Ulrich Rührmair, Frank Sehnke, Jan Sölter, Gideon Dror, Srinivas Devadas, Jürgen Schmidhuber:
Modeling attacks on physical unclonable functions. CCS 2010: 237-249 - [c107]Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber:
Exponential natural evolution strategies. GECCO 2010: 393-400 - [c106]Jan Koutník, Faustino J. Gomez, Jürgen Schmidhuber:
Evolving neural networks in compressed weight space. GECCO 2010: 619-626 - [c105]Mandy Grüttner, Frank Sehnke, Tom Schaul, Jürgen Schmidhuber:
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. ICANN (2) 2010: 114-123 - [c104]Frank Sehnke, Christian Osendorfer, Jan Sölter, Jürgen Schmidhuber, Ulrich Rührmair:
Policy Gradients for Cryptanalysis. ICANN (3) 2010: 168-177 - [c103]Frank Sehnke, Alex Graves, Christian Osendorfer, Jürgen Schmidhuber:
Multimodal Parameter-exploring Policy Gradients. ICMLA 2010: 113-118 - [c102]Matteo Gagliolo, Jürgen Schmidhuber:
Algorithm Selection as a Bandit Problem with Unbounded Losses. LION 2010: 82-96 - [c101]Yi Sun, Faustino J. Gomez, Jürgen Schmidhuber:
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices. NIPS 2010: 2235-2243 - [c100]Jürgen Schmidhuber:
Formal Theory of Fun and Creativity. ECML/PKDD (1) 2010: 6 - [c99]Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber:
Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. ECML/PKDD (1) 2010: 264-279 - [c98]Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber:
A Natural Evolution Strategy for Multi-objective Optimization. PPSN (1) 2010: 627-636 - [i34]Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber:
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition. CoRR abs/1003.0358 (2010) - [i33]Jürgen Schmidhuber:
Evolution of National Nobel Prize Shares in the 20th Century. CoRR abs/1009.2634 (2010) - [i32]Ulrich Rührmair, Frank Sehnke, Jan Sölter, Gideon Dror, Srinivas Devadas, Jürgen Schmidhuber:
Modeling Attacks on Physical Unclonable Functions. IACR Cryptol. ePrint Arch. 2010: 251 (2010)
2000 – 2009
- 2009
- [j39]Daniil Ryabko, Jürgen Schmidhuber:
Using data compressors to construct order tests for homogeneity and component independence. Appl. Math. Lett. 22(7): 1029-1032 (2009) - [j38]Jürgen Schmidhuber:
Ultimate Cognition à la Gödel. Cogn. Comput. 1(2): 177-193 (2009) - [j37]José David Martín-Guerrero, Faustino J. Gomez, Emilio Soria-Olivas, Jürgen Schmidhuber, Mónica Climente-Martí, N. Víctor Jiménez-Torres:
A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients. Expert Syst. Appl. 36(6): 9737-9742 (2009) - [j36]Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius:
Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. 23(3): 30-33 (2009) - [j35]Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, Jürgen Schmidhuber:
A Novel Connectionist System for Unconstrained Handwriting Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5): 855-868 (2009) - [c97]Niels van Hoorn, Julian Togelius, Daan Wierstra, Jürgen Schmidhuber:
Robust player imitation using multiobjective evolution. IEEE Congress on Evolutionary Computation 2009: 652-659 - [c96]Julian Togelius, Sergey Karakovskiy, Jan Koutník, Jürgen Schmidhuber:
Super mario evolution. CIG 2009: 156-161 - [c95]Niels van Hoorn, Julian Togelius, Jürgen Schmidhuber:
Hierarchical controller learning in a First-Person Shooter. CIG 2009: 294-301 - [c94]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient natural evolution strategies. GECCO 2009: 539-546 - [c93]Justin Bayer, Daan Wierstra, Julian Togelius, Jürgen Schmidhuber:
Evolving Memory Cell Structures for Sequence Learning. ICANN (2) 2009: 755-764 - [c92]Faustino J. Gomez, Julian Togelius, Jürgen Schmidhuber:
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning. ICANN (2) 2009: 765-774 - [c91]Jan Unkelbach, Yi Sun, Jürgen Schmidhuber:
An EM Based Training Algorithm for Recurrent Neural Networks. ICANN (1) 2009: 964-974 - [c90]Tom Schaul, Jürgen Schmidhuber:
Scalable Neural Networks for Board Games. ICANN (1) 2009: 1005-1014 - [c89]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Stochastic search using the natural gradient. ICML 2009: 1161-1168 - [i31]Jürgen Schmidhuber:
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes. Computational Creativity: An Interdisciplinary Approach 2009 - [i30]György Csaba, Xueming Ju, Qingqing Chen, Wolfgang Porod, Jürgen Schmidhuber, Ulf Schlichtmann, Paolo Lugli, Ulrich Rührmair:
On-Chip Electric Waves: An Analog Circuit Approach to Physical Uncloneable Functions. IACR Cryptol. ePrint Arch. 2009: 246 (2009) - 2008
- [j34]Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber:
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Adv. Robotics 22(13-14): 1521-1537 (2008) - [j33]Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen:
Accelerated Neural Evolution through Cooperatively Coevolved Synapses. J. Mach. Learn. Res. 9: 937-965 (2008) - [c88]Jürgen Schmidhuber:
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes. ABiALS 2008: 48-76 - [c87]Julian Togelius, Faustino J. Gomez, Jürgen Schmidhuber:
Learning what to ignore: Memetic climbing in topology and weight space. IEEE Congress on Evolutionary Computation 2008: 3274-3281 - [c86]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Natural Evolution Strategies. IEEE Congress on Evolutionary Computation 2008: 3381-3387 - [c85]Julian Togelius, Jürgen Schmidhuber:
An experiment in automatic game design. CIG 2008: 111-118 - [c84]Alexandros Agapitos, Julian Togelius, Simon M. Lucas, Jürgen Schmidhuber, Andreas Konstantinidis:
Generating diverse opponents with multiobjective evolution. CIG 2008: 135-142 - [c83]Tom Schaul, Jürgen Schmidhuber:
A scalable neural network architecture for board games. CIG 2008: 357-364 - [c82]Matteo Gagliolo, Jürgen Schmidhuber:
Towards Distributed Algorithm Portfolios. DCAI 2008: 634-643 - [c81]Frank Sehnke, Christian Osendorfer, Thomas Rückstieß, Alex Graves, Jan Peters, Jürgen Schmidhuber:
Policy Gradients with Parameter-Based Exploration for Control. ICANN (1) 2008: 387-396 - [c80]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) 2008: 407-416 - [c79]Jürgen Schmidhuber:
Driven by Compression Progress. KES (1) 2008: 11 - [c78]Alex Graves, Jürgen Schmidhuber:
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. NIPS 2008: 545-552 - [c77]Thomas Rückstieß, Martin Felder, Jürgen Schmidhuber:
State-Dependent Exploration for Policy Gradient Methods. ECML/PKDD (2) 2008: 234-249 - [c76]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Fitness Expectation Maximization. PPSN 2008: 337-346 - [c75]Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino J. Gomez:
Countering Poisonous Inputs with Memetic Neuroevolution. PPSN 2008: 610-619 - [i29]Santiago Fernández, Alex Graves, Jürgen Schmidhuber:
Phoneme recognition in TIMIT with BLSTM-CTC. CoRR abs/0804.3269 (2008) - [i28]Matteo Gagliolo, Jürgen Schmidhuber:
Algorithm Selection as a Bandit Problem with Unbounded Losses. CoRR abs/0807.1494 (2008) - [i27]Jürgen Schmidhuber:
Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes. CoRR abs/0812.4360 (2008) - 2007
- [j32]Alexey V. Chernov, Marcus Hutter, Jürgen Schmidhuber:
Algorithmic complexity bounds on future prediction errors. Inf. Comput. 205(2): 242-261 (2007) - [j31]Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, Faustino J. Gomez:
Training Recurrent Networks by Evolino. Neural Comput. 19(3): 757-779 (2007) - [c74]Jürgen Schmidhuber:
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity. ALT 2007: 32-33 - [c73]Jürgen Schmidhuber:
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity. Discovery Science 2007: 26-38 - [c72]Daan Wierstra, Jürgen Schmidhuber:
Policy Gradient Critics. ECML 2007: 466-477 - [c71]Alexander Förster, Alex Graves, Jürgen Schmidhuber:
RNN-based Learning of Compact Maps for Efficient Robot Localization. ESANN 2007: 537-542 - [c70]Santiago Fernández, Alex Graves, Jürgen Schmidhuber:
An Application of Recurrent Neural Networks to Discriminative Keyword Spotting. ICANN (2) 2007: 220-229 - [c69]Alex Graves, Santiago Fernández, Jürgen Schmidhuber:
Multi-dimensional Recurrent Neural Networks. ICANN (1) 2007: 549-558 - [c68]Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber:
Solving Deep Memory POMDPs with Recurrent Policy Gradients. ICANN (1) 2007: 697-706 - [c67]Santiago Fernández, Alex Graves, Jürgen Schmidhuber:
Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks. IJCAI 2007: 774-779 - [c66]Matteo Gagliolo, Jürgen Schmidhuber:
Learning Restart Strategies. IJCAI 2007: 792-797 - [c65]Alex Graves, Santiago Fernández, Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber:
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks. NIPS 2007: 577-584 - [p4]Jürgen Schmidhuber:
The New AI: General & Sound & Relevant for Physics. Artificial General Intelligence 2007: 175-198 - [p3]Jürgen Schmidhuber:
Gödel Machines: Fully Self-referential Optimal Universal Self-improvers. Artificial General Intelligence 2007: 199-226 - [p2]Jürgen Schmidhuber:
New Millennium AI and the Convergence of History. Challenges for Computational Intelligence 2007: 15-35 - [i26]Alex Graves, Santiago Fernández, Jürgen Schmidhuber:
Multi-Dimensional Recurrent Neural Networks. CoRR abs/0705.2011 (2007) - [i25]Jürgen Schmidhuber:
2006: Celebrating 75 years of AI - History and Outlook: the Next 25 Years. CoRR abs/0708.4311 (2007) - [i24]Daniil Ryabko, Jürgen Schmidhuber:
Using Data Compressors to Construct Rank Tests. CoRR abs/0709.0670 (2007) - [i23]Jürgen Schmidhuber:
Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity. CoRR abs/0709.0674 (2007) - [i22]Alexey V. Chernov, Marcus Hutter, Jürgen Schmidhuber:
Algorithmic Complexity Bounds on Future Prediction Errors. CoRR abs/cs/0701120 (2007) - 2006
- [j30]Matteo Gagliolo, Jürgen Schmidhuber:
Learning dynamic algorithm portfolios. Ann. Math. Artif. Intell. 47(3-4): 295-328 (2006) - [j29]Jürgen Schmidhuber:
Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connect. Sci. 18(2): 173-187 (2006) - [c64]Jürgen Schmidhuber:
2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years. 50 Years of Artificial Intelligence 2006: 29-41 - [c63]Alexey V. Chernov, Jürgen Schmidhuber:
Prefix-Like Complexities and Computability in the Limit. CiE 2006: 85-93 - [c62]Matteo Gagliolo, Jürgen Schmidhuber:
Impact of Censored Sampling on the Performance of Restart Strategies. CP 2006: 167-181 - [c61]Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen:
Efficient Non-linear Control Through Neuroevolution. ECML 2006: 654-662 - [c60]Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez:
Evolino for recurrent support vector machines. ESANN 2006: 593-598 - [c59]Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter, Jürgen Schmidhuber:
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot. IAS 2006: 272-281 - [c58]Alex Graves, Santiago Fernández, Faustino J. Gomez, Jürgen Schmidhuber:
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. ICML 2006: 369-376 - [c57]Bram Bakker, Viktor Zhumatiy, Gabriel Gruener, Jürgen Schmidhuber:
Quasi-online Reinforcement Learning for Robots. ICRA 2006: 2997-3002 - [c56]Hermann Georg Mayer, Faustino J. Gomez, Daan Wierstra, Istvan Nagy, Alois C. Knoll, Jürgen Schmidhuber:
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. IROS 2006: 543-548 - [c55]Matteo Gagliolo, Jürgen Schmidhuber:
Dynamic Algorithm Portfolios. AI&M 2006 - [i21]Alexey V. Chernov, Marcus Hutter, Jürgen Schmidhuber:
Complexity Monotone in Conditions and Future Prediction Errors. Kolmogorov Complexity and Applications 2006 - [i20]Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter, Jürgen Schmidhuber:
Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot. CoRR abs/cs/0603023 (2006) - [i19]Jürgen Schmidhuber:
New Millennium AI and the Convergence of History. CoRR abs/cs/0606081 (2006) - 2005
- [j28]Alex Graves, Jürgen Schmidhuber:
Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5-6): 602-610 (2005) - [c54]Jürgen Schmidhuber:
How to Learn a Program: Optimal Universal Learners & Goedel Machines. AAIP 2005: 11 - [c53]Jürgen Schmidhuber:
Gödel Machines: Towards a Technical Justification of Consciousness. Adaptive Agents and Multi-Agent Systems 2005: 1-23 - [c52]Faustino J. Gomez, Jürgen Schmidhuber:
Co-evolving recurrent neurons learn deep memory POMDPs. GECCO 2005: 491-498 - [c51]Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber:
Modeling systems with internal state using evolino. GECCO 2005: 1795-1802 - [c50]Matteo Gagliolo, Jürgen Schmidhuber:
A Neural Network Model for Inter-problem Adaptive Online Time Allocation. ICANN (2) 2005: 7-12 - [c49]Jürgen Schmidhuber:
Completely Self-referential Optimal Reinforcement Learners. ICANN (2) 2005: 223-233 - [c48]Faustino J. Gomez, Jürgen Schmidhuber:
Evolving Modular Fast-Weight Networks for Control. ICANN (2) 2005: 383-389 - [c47]Martijn van de Giessen, Jürgen Schmidhuber:
Fast Color-Based Object Recognition Independent of Position and Orientation. ICANN (1) 2005: 469-474 - [c46]Nicole Beringer, Alex Graves, Florian Schiel, Jürgen Schmidhuber:
Classifying Unprompted Speech by Retraining LSTM Nets. ICANN (1) 2005: 575-581 - [c45]Alex Graves, Santiago Fernández, Jürgen Schmidhuber:
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. ICANN (2) 2005: 799-804 - [c44]Jürgen Schmidhuber, Daan Wierstra, Faustino J. Gomez:
Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. IJCAI 2005: 853-858 - [i18]Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez:
Evolino for recurrent support vector machines. CoRR abs/cs/0512062 (2005) - 2004
- [j27]Jürgen Schmidhuber:
Optimal Ordered Problem Solver. Mach. Learn. 54(3): 211-254 (2004) - [j26]Michele Milano, Petros Koumoutsakos, Jürgen Schmidhuber:
Self-organizing nets for optimization. IEEE Trans. Neural Networks 15(3): 758-765 (2004) - [c43]Alex Graves, Douglas Eck, Nicole Beringer, Jürgen Schmidhuber:
Biologically Plausible Speech Recognition with LSTM Neural Nets. BioADIT 2004: 127-136 - [c42]Matteo Gagliolo, Viktor Zhumatiy, Jürgen Schmidhuber:
Adaptive Online Time Allocation to Search Algorithms. ECML 2004: 134-143 - [c41]Bram Bakker, Jürgen Schmidhuber:
Hierarchical reinforcement learning with subpolicies specializing for learned subgoals. Neural Networks and Computational Intelligence 2004: 125-130 - [c40]Alex Graves, Nicole Beringer, Jürgen Schmidhuber:
A comparison between spiking and differentiable recurrent neural networks on spoken digit recognition. Neural Networks and Computational Intelligence 2004: 164-168 - 2003
- [j25]Juan Antonio Pérez-Ortiz, Felix A. Gers, Douglas Eck, Jürgen Schmidhuber:
Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks 16(2): 241-250 (2003) - [c39]Bram Bakker, Viktor Zhumatiy, Gabriel Gruener, Jürgen Schmidhuber:
A robot that reinforcement-learns to identify and memorize important previous observations. IROS 2003: 430-435 - [i17]Jürgen Schmidhuber:
The New AI: General & Sound & Relevant for Physics. CoRR cs.AI/0302012 (2003) - [i16]Jürgen Schmidhuber:
Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements. CoRR cs.LO/0309048 (2003) - 2002
- [j24]Jürgen Schmidhuber:
Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit. Int. J. Found. Comput. Sci. 13(4): 587-612 (2002) - [j23]Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber:
Learning Precise Timing with LSTM Recurrent Networks. J. Mach. Learn. Res. 3: 115-143 (2002) - [j22]Jürgen Schmidhuber, Felix A. Gers, Douglas Eck:
Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM. Neural Comput. 14(9): 2039-2041 (2002) - [c38]Jürgen Schmidhuber:
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions. COLT 2002: 216-228 - [c37]Felix A. Gers, Juan Antonio Pérez-Ortiz, Douglas Eck, Jürgen Schmidhuber:
DEKF-LSTM. ESANN 2002: 369-376 - [c36]Douglas Eck, Jürgen Schmidhuber:
Learning the Long-Term Structure of the Blues. ICANN 2002: 284-289 - [c35]Felix A. Gers, Juan Antonio Pérez-Ortiz, Douglas Eck, Jürgen Schmidhuber:
Learning Context Sensitive Languages with LSTM Trained with Kalman Filters. ICANN 2002: 655-660 - [c34]Juan Antonio Pérez-Ortiz, Jürgen Schmidhuber, Felix A. Gers, Douglas Eck:
Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters. ICANN 2002: 1055-1069 - [c33]Bram Bakker, Fredrik Linåker, Jürgen Schmidhuber:
Reinforcement learning in partially observable mobile robot domains using unsupervised event extraction. IROS 2002: 938-943 - [c32]Jürgen Schmidhuber:
Bias-Optimal Incremental Problem Solving. NIPS 2002: 1547-1546 - [c31]Douglas Eck, Jürgen Schmidhuber:
Finding temporal structure in music: blues improvisation with LSTM recurrent networks. NNSP 2002: 747-756 - [i15]Jürgen Schmidhuber:
Optimal Ordered Problem Solver. CoRR cs.AI/0207097 (2002) - 2001
- [j21]Ivo Kwee, Jürgen Schmidhuber:
Optimal Control Using the Transport Equation: The Liouville Machine. Adapt. Behav. 9(2): 105-118 (2001) - [j20]Felix A. Gers, Jürgen Schmidhuber:
LSTM recurrent networks learn simple context-free and context-sensitive languages. IEEE Trans. Neural Networks 12(6): 1333-1340 (2001) - [c30]Michele Milano, Jürgen Schmidhuber, Petros Koumoutsakos:
Active Learning with Adaptive Grids. ICANN 2001: 436-442 - [c29]Felix A. Gers, Douglas Eck, Jürgen Schmidhuber:
Applying LSTM to Time Series Predictable through Time-Window Approaches. ICANN 2001: 669-676 - [c28]Magdalena Klapper-Rybicka, Nicol N. Schraudolph, Jürgen Schmidhuber:
Unsupervised Learning in LSTM Recurrent Neural Networks. ICANN 2001: 684-691 - [c27]Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber:
Market-Based Reinforcement Learning in Partially Observable Worlds. ICANN 2001: 865-873 - [c26]Jürgen Schmidhuber:
Sequential Decision Making Based on Direct Search. Sequence Learning 2001: 213-240 - [c25]Felix A. Gers, Douglas Eck, Jürgen Schmidhuber:
Applying LSTM to Time Series Predictable Through Time-Window Approaches. WIRN 2001: 193-200 - [i14]Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber:
Market-Based Reinforcement Learning in Partially Observable Worlds. CoRR cs.AI/0105025 (2001) - [i13]Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber:
Gradient-based Reinforcement Planning in Policy-Search Methods. CoRR cs.AI/0111060 (2001) - 2000
- [j19]Felix A. Gers, Jürgen Schmidhuber, Fred A. Cummins:
Learning to Forget: Continual Prediction with LSTM. Neural Comput. 12(10): 2451-2471 (2000) - [c24]Michele Milano, Petros Koumoutsakos, Xavier Giannakopoulos, Jürgen Schmidhuber:
Evolving strategies for active flow control. CEC 2000: 212-218 - [c23]Felix A. Gers, Jürgen Schmidhuber:
Recurrent Nets that Time and Count. IJCNN (3) 2000: 189-194 - [c22]Felix A. Gers, Jürgen Schmidhuber:
Neural Processing of Complex Continual Input Streams. IJCNN (4) 2000: 557-562 - [i12]Jürgen Schmidhuber:
Algorithmic Theories of Everything. CoRR quant-ph/0011122 (2000)
1990 – 1999
- 1999
- [j18]Marco A. Wiering, Rafal Salustowicz, Jürgen Schmidhuber:
Reinforcement Learning Soccer Teams with Incomplete World Models. Auton. Robots 7(1): 77-88 (1999) - [j17]Sepp Hochreiter, Jürgen Schmidhuber:
Feature Extraction Through LOCOCODE. Neural Comput. 11(3): 679-714 (1999) - [c21]Jürgen Schmidhuber:
Artificial curiosity based on discovering novel algorithmic predictability through coevolution. CEC 1999: 1612-1618 - [c20]Fred A. Cummins, Felix A. Gers, Jürgen Schmidhuber:
Language identification from prosody without explicit features. EUROSPEECH 1999: 371-374 - [c19]Sepp Hochreiter, Jürgen Schmidhuber:
Nonlinear ICA through low-complexity autoencoders. ISCAS (5) 1999: 53-56 - [i11]Jürgen Schmidhuber:
A Computer Scientist's View of Life, the Universe, and Everything. CoRR quant-ph/9904050 (1999) - 1998
- [j16]Marco A. Wiering, Jürgen Schmidhuber:
Fast Online Q(lambda). Mach. Learn. 33(1): 105-115 (1998) - [j15]Rafal Salustowicz, Marco A. Wiering, Jürgen Schmidhuber:
Learning Team Strategies: Soccer Case Studies. Mach. Learn. 33(2-3): 263-282 (1998) - [c18]Marco A. Wiering, Jürgen Schmidhuber:
Speeding up Q(lambda)-Learning. ECML 1998: 352-363 - [c17]Rafal Salustowicz, Jürgen Schmidhuber:
Evolving Structured Programs with Hierarchical Instructions and Skip Nodes. ICML 1998: 488-496 - [c16]Sepp Hochreiter, Jürgen Schmidhuber:
Source Separation as a By-Product of Regularization. NIPS 1998: 459-465 - [p1]Jürgen Schmidhuber, Jieyu Zhao, Nicol N. Schraudolph:
Reinforcement Learning with Self-Modifying Policies. Learning to Learn 1998: 293-309 - 1997
- [j14]Marco A. Wiering, Jürgen Schmidhuber:
HQ-Learning. Adapt. Behav. 6(2): 219-246 (1997) - [j13]Rafal Salustowicz, Jürgen Schmidhuber:
Probabilistic Incremental Program Evolution. Evol. Comput. 5(2): 123-141 (1997) - [j12]Jürgen Schmidhuber, Jieyu Zhao, Marco A. Wiering:
Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement. Mach. Learn. 28(1): 105-130 (1997) - [j11]Sepp Hochreiter, Jürgen Schmidhuber:
Flat Minima. Neural Comput. 9(1): 1-42 (1997) - [j10]Sepp Hochreiter, Jürgen Schmidhuber:
Long Short-Term Memory. Neural Comput. 9(8): 1735-1780 (1997) - [j9]Jürgen Schmidhuber:
Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability. Neural Networks 10(5): 857-873 (1997) - [c15]Jürgen Schmidhuber:
A Computer Scientist's View of Life, the Universe, and Everything. Foundations of Computer Science: Potential - Theory - Cognition 1997: 201-208 - [c14]Rafal Salustowicz, Jürgen Schmidhuber:
Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space. ECML 1997: 213-220 - [c13]Sepp Hochreiter, Jürgen Schmidhuber:
Unsupervised Coding with LOCOCODE. ICANN 1997: 655-660 - [c12]Rafal Salustowicz, Marco A. Wiering, Jürgen Schmidhuber:
On Learning Soccer Strategies. ICANN 1997: 769-774 - [c11]Rafal Salustowicz, Marco A. Wiering, Jürgen Schmidhuber:
Evolving Soccer Strategies. ICONIP (1) 1997: 502-505 - 1996
- [j8]Jürgen Schmidhuber, Martin Eldracher, Bernhard Foltin:
Semilinear Predictability Minimization Produces Well-Known Feature Detectors. Neural Comput. 8(4): 773-786 (1996) - [j7]Jürgen Schmidhuber, Stefan Heil:
Sequential neural text compression. IEEE Trans. Neural Networks 7(1): 142-146 (1996) - [c10]Jürgen Schmidhuber, Jieyu Zhao:
Multi-Agent Learning with the Success-Story Algorithm. ECAI Workshop LDAIS / ICMAS Workshop LIOME 1996: 82-93 - [c9]Marco A. Wiering, Jürgen Schmidhuber:
Solving POMDPs with Levin Search and EIRA. ICML 1996: 534-542 - [c8]Sepp Hochreiter, Jürgen Schmidhuber:
LSTM can Solve Hard Long Time Lag Problems. NIPS 1996: 473-479 - 1995
- [c7]Jürgen Schmidhuber:
Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability. ICML 1995: 488-496 - 1994
- [c6]Sepp Hochreiter, Jürgen Schmidhuber:
Simplifying Neural Nets by Discovering Flat Minima. NIPS 1994: 529-536 - [c5]Jürgen Schmidhuber, Stefan Heil:
Predictive Coding with Neural Nets: Application to Text Compression. NIPS 1994: 1047-1054 - 1993
- [b3]Jürgen Schmidhuber:
Netzwerkarchitekturen, Zielfunktionen und Kettenregel (Network architectures, objective functions, and chain rule). Technical University of Munich, Germany, 1993 - [j6]Jürgen Schmidhuber, Daniel Prelinger:
Discovering Predictable Classifications. Neural Comput. 5(4): 625-635 (1993) - [c4]Jürgen Schmidhuber:
A neural network that embeds its own meta-levels. ICNN 1993: 407-412 - 1992
- [j5]Jürgen Schmidhuber:
Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks. Neural Comput. 4(1): 131-139 (1992) - [j4]Jürgen Schmidhuber:
Learning Complex, Extended Sequences Using the Principle of History Compression. Neural Comput. 4(2): 234-242 (1992) - [j3]Jürgen Schmidhuber:
A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks. Neural Comput. 4(2): 243-248 (1992) - [j2]Jürgen Schmidhuber:
Learning Factorial Codes by Predictability Minimization. Neural Comput. 4(6): 863-879 (1992) - 1991
- [j1]Jürgen Schmidhuber, Rudolf Huber:
Learning to Generate Artificial Fovea Trajectories for Target Detection. Int. J. Neural Syst. 2(1-2): 125-134 (1991) - [c3]Jürgen Schmidhuber:
Learning Unambiguous Reduced Sequence Descriptions. NIPS 1991: 291-298 - [i10]Jürgen Schmidhuber:
Learning to control fast-weight memories: an alternative to dynamic recurrent networks. Forschungsberichte, TU Munich FKI 147 91: 1-6 (1991) - [i9]Jürgen Schmidhuber:
Neural sequence chunkers. Forschungsberichte, TU Munich FKI 148 91: 1-17 (1991) - [i8]Jürgen Schmidhuber:
Adaptive confidence and adaptive curiosity. Forschungsberichte, TU Munich FKI 149 91: 1-9 (1991) - [i7]Jürgen Schmidhuber:
An O(n3) learning algorithm for fully recurrent networks. Forschungsberichte, TU Munich FKI 151 91: 1-4 (1991) - 1990
- [b2]Jürgen Schmidhuber:
Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dynamic neural nets and the fundamental spatio-temporal credit assignment problem). TU Munich, Germany, 1990 - [b1]Jürgen Schmidhuber:
Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem. Technical University Munich, Germany, 1990 - [c2]Jürgen Schmidhuber:
An on-line algorithm for dynamic reinforcement learning and planning in reactive environments. IJCNN 1990: 253-258 - [c1]Jürgen Schmidhuber:
Reinforcement Learning in Markovian and Non-Markovian Environments. NIPS 1990: 500-506 - [i6]Jürgen Schmidhuber:
A local learning algorithm for dynamic feedforward and recurrent networks. Forschungsberichte, TU Munich FKI 124 90: 1-7 (1990) - [i5]Jürgen Schmidhuber:
Networks adjusting networks. Forschungsberichte, TU Munich FKI 125 90: 1-13 (1990) - [i4]Jürgen Schmidhuber:
Making the world differentiable: on using self supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments. Forschungsberichte, TU Munich FKI 126 90: 1-26 (1990) - [i3]Jürgen Schmidhuber:
Learning to generate focus trajectories for attentive vision. Forschungsberichte, TU Munich FKI 128 90: 1-13 (1990) - [i2]Jürgen Schmidhuber:
Towards compositional learning with dynamic neural networks. Forschungsberichte, TU Munich FKI 129 90: 1-9 (1990)
1980 – 1989
- 1987
- [i1]Jürgen Schmidhuber:
Evolutionary principles in self-referential learning, or on learning how to learn: The meta-meta-. hook. Technical University of Munich, Germany, 1987
Coauthor Index
Dan C. Ciresan
aka: Dan Claudiu Ciresan
aka: Dan Claudiu Ciresan
[c184] [c182] [c178] [c173] [c170] [c169] [i64] [i62] [c166] [c165] [c164] [c155] [c151] [c149] [c141] [c135] [c134] [i53] [i51] [i44] [c129] [c125] [c119] [c116] [c112] [i41] [i40] [c106] [c101] [j37] [j36] [c92] [j34] [j33] [c87] [c75] [j31] [c61] [c60] [c59] [c58] [c56] [i20] [c52] [c51] [c48] [c44] [i18]
Matthew D. Luciw
aka: Matthew David Luciw
aka: Matthew David Luciw
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