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Jonas Kohler
Person information
- affiliation: ETH Zurich, Zurich, Switzerland
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2020 – today
- 2024
- [i17]Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, Ali K. Thabet:
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation. CoRR abs/2405.05224 (2024) - 2023
- [i16]Felix Wimbauer, Bichen Wu, Edgar Schönfeld, Xiaoliang Dai, Ji Hou, Zijian He, Artsiom Sanakoyeu, Peizhao Zhang, Sam S. Tsai, Jonas Kohler, Christian Rupprecht, Daniel Cremers, Peter Vajda, Jialiang Wang:
Cache Me if You Can: Accelerating Diffusion Models through Block Caching. CoRR abs/2312.03209 (2023) - [i15]Angela Castillo, Jonas Kohler, Juan C. Pérez, Juan Pablo Pérez, Albert Pumarola, Bernard Ghanem, Pablo Arbeláez, Ali K. Thabet:
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models. CoRR abs/2312.12487 (2023) - [i14]Jonas Kohler, Nicolas Griffiths Sanchez, Luca Cavalli, Catherine Herold, Albert Pumarola, Alberto Garcia Garcia, Ali K. Thabet:
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations. CoRR abs/2312.16109 (2023) - 2022
- [b1]Jonas Kohler:
Insights on the interplay of network architectures and optimization algorithms in deep learning. ETH Zurich, Zürich, Switzerland, 2022 - [c7]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - 2021
- [c6]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi
:
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV 2021: 13859-13869 - [i13]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. CoRR abs/2103.15627 (2021) - [i12]Adrian Hoffmann, Claudio Fanconi, Rahul Rade, Jonas Kohler:
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks. CoRR abs/2105.02968 (2021) - [i11]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR abs/2106.03763 (2021) - [i10]Ziyad Sheebaelhamd, Konstantinos Zisis, Athina Nisioti, Dimitris Gkouletsos, Dario Pavllo, Jonas Kohler:
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces. CoRR abs/2108.03952 (2021) - [i9]Jonas Kohler, Maarten C. Ottenhoff, Sophocles Goulis, Miguel Angrick, Albert J. Colon, G. Louis Wagner, Simon Tousseyn, Pieter Leonard Kubben, Christian Herff:
Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework. CoRR abs/2111.01457 (2021) - 2020
- [c5]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS 2020 - [i8]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR abs/2003.01652 (2020) - [i7]Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto:
Two-Level K-FAC Preconditioning for Deep Learning. CoRR abs/2011.00573 (2020)
2010 – 2019
- 2019
- [c4]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr:
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. AISTATS 2019: 806-815 - [c3]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. UAI 2019: 356-366 - [i6]Leonard Adolphs, Jonas Kohler, Aurélien Lucchi:
Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. CoRR abs/1905.09201 (2019) - [i5]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. CoRR abs/1907.01678 (2019) - [i4]Aurélien Lucchi, Jonas Kohler:
A Stochastic Tensor Method for Non-convex Optimization. CoRR abs/1911.10367 (2019) - 2018
- [c2]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. ICML 2018: 1163-1172 - [i3]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. CoRR abs/1803.05999 (2018) - [i2]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann:
Towards a Theoretical Understanding of Batch Normalization. CoRR abs/1805.10694 (2018) - 2017
- [c1]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. ICML 2017: 1895-1904 - [i1]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. CoRR abs/1705.05933 (2017)
Coauthor Index
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