default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFei Sha
Person information
- affiliation: University of Southern California, Los Angeles, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c122]Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk:
V2Meow: Meowing to the Visual Beat via Video-to-Music Generation. AAAI 2024: 4952-4960- [c121]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. ICML 2024 - [c120]Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, Tal Linzen:
The Impact of Depth on Compositional Generalization in Transformer Language Models. NAACL-HLT 2024: 7239-7252 - [c119]Tiwalayo Eisape, Michael Henry Tessler, Ishita Dasgupta, Fei Sha, Sjoerd van Steenkiste, Tal Linzen:
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. NAACL-HLT 2024: 8425-8444 - [i81]Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez:
DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems. CoRR abs/2402.04467 (2024) - [i80]Benedikt Barthel Sorensen, Leonardo Zepeda-Núñez, Ignacio Lopez-Gomez, Zhong Yi Wan, Rob Carver, Fei Sha, Themistoklis P. Sapsis:
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data. CoRR abs/2408.02688 (2024) - [i79]Shantanu Shahane, Sheide Chammas, Deniz A. Bezgin, Aaron B. Buhendwa, Steffen J. Schmidt, Nikolaus A. Adams, Spencer H. Bryngelson, Yi-Fan Chen, Qing Wang, Fei Sha, Leonardo Zepeda-Núñez:
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme. CoRR abs/2409.09217 (2024) - [i78]Roberto Molinaro, Samuel Lanthaler, Bogdan Raonic, Tobias Rohner, Victor Armegioiu, Zhong Yi Wan, Fei Sha, Siddhartha Mishra, Leonardo Zepeda-Núñez:
Generative AI for fast and accurate Statistical Computation of Fluids. CoRR abs/2409.18359 (2024) - 2023
- [j15]Hexiang Hu
, Ozan Sener, Fei Sha, Vladlen Koltun:
Drinking From a Firehose: Continual Learning With Web-Scale Natural Language. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 5684-5696 (2023) - [c118]Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, William W. Cohen:
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference. ACL (Findings) 2023: 11534-11547 - [c117]Thomas Mensink, Jasper R. R. Uijlings, Lluís Castrejón, Arushi Goel, Felipe Cadar, Howard Zhou, Fei Sha, André Araújo, Vittorio Ferrari:
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories. ICCV 2023: 3090-3101 - [c116]Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha:
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. ICLR 2023 - [c115]Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen:
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. ICML 2023: 7329-7342 - [c114]Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. ICML 2023: 10136-10152 - [c113]Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez, James Lottes, Qing Wang, Yi-Fan Chen, John Anderson, Fei Sha:
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. NeurIPS 2023 - [c112]Zhong Yi Wan, Ricardo Baptista, Anudhyan Boral, Yi-Fan Chen, John Anderson, Fei Sha, Leonardo Zepeda-Núñez:
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. NeurIPS 2023 - [i77]Zhong Yi Wan, Leonardo Zepeda-Núñez, Anudhyan Boral, Fei Sha:
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems. CoRR abs/2301.10391 (2023) - [i76]Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Joshua Ainslie, Sumit Sanghai, Fei Sha, William W. Cohen:
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute. CoRR abs/2301.10448 (2023) - [i75]Sébastien M. R. Arnold, Fei Sha:
Policy-Induced Self-Supervision Improves Representation Finetuning in Visual RL. CoRR abs/2302.06009 (2023) - [i74]Kun Su, Judith Yue Li, Qingqing Huang, Dima Kuzmin, Joonseok Lee, Chris Donahue, Fei Sha, Aren Jansen, Yu Wang, Mauro Verzetti, Timo I. Denk:
V2Meow: Meowing to the Visual Beat via Music Generation. CoRR abs/2305.06594 (2023) - [i73]Zhong Yi Wan, Ricardo Baptista, Yi-Fan Chen, John Anderson, Anudhyan Boral, Fei Sha, Leonardo Zepeda-Núñez:
Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models. CoRR abs/2305.15618 (2023) - [i72]Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Núñez, James Lottes, Qing Wang, Yi-Fan Chen, John Robert Anderson, Fei Sha:
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations. CoRR abs/2306.01174 (2023) - [i71]Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. CoRR abs/2306.07526 (2023) - [i70]Thomas Mensink, Jasper R. R. Uijlings, Lluís Castrejón, Arushi Goel, Felipe Cadar, Howard Zhou, Fei Sha, André Araújo, Vittorio Ferrari:
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories. CoRR abs/2306.09224 (2023) - [i69]Lizao Li, Rob Carver, Ignacio Lopez-Gomez, Fei Sha, John Anderson:
SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models. CoRR abs/2306.14066 (2023) - [i68]Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter W. Battaglia, Tyler Russell, Alvaro Sanchez-Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, Matthew Chantry, Zied Ben Bouallegue, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, Fei Sha:
WeatherBench 2: A benchmark for the next generation of data-driven global weather models. CoRR abs/2308.15560 (2023) - [i67]Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta, Fei Sha, Dan Garrette, Tal Linzen:
The Impact of Depth and Width on Transformer Language Model Generalization. CoRR abs/2310.19956 (2023) - [i66]Tiwalayo Eisape, Michael Henry Tessler, Ishita Dasgupta, Fei Sha, Sjoerd van Steenkiste, Tal Linzen:
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models. CoRR abs/2311.00445 (2023) - 2022
- [j14]Fei Sha, Ruizhi Zhang:
Adversarially robust subspace learning in the spiked covariance model. Stat. Anal. Data Min. 15(4): 521-530 (2022) - [c111]Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha:
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. AISTATS 2022: 1041-1061 - [c110]Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha:
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing. COLING 2022: 4946-4951 - [c109]Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova:
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. EMNLP 2022: 9157-9179 - [c108]Robby Costales, Shariq Iqbal, Fei Sha:
Possibility Before Utility: Learning And Using Hierarchical Affordances. ICLR 2022 - [c107]Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William W. Cohen:
Mention Memory: incorporating textual knowledge into Transformers through entity mention attention. ICLR 2022 - [c106]Fei Sha, Ruizhi Zhang:
Quickest Detection of the Change of Community via Stochastic Block Models. ISIT 2022: 1903-1908 - [c105]Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Krzysztof Nowak, Tal Linzen, Fei Sha, Kristina Toutanova:
Improving Compositional Generalization with Latent Structure and Data Augmentation. NAACL-HLT 2022: 4341-4362 - [c104]Shariq Iqbal, Robby Costales, Fei Sha:
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. NeurIPS 2022 - [i65]Sébastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen, Yi-Fan Chen, Fei Sha:
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. CoRR abs/2202.07808 (2022) - [i64]Robby Costales, Shariq Iqbal, Fei Sha:
Possibility Before Utility: Learning And Using Hierarchical Affordances. CoRR abs/2203.12686 (2022) - [i63]Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova:
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. CoRR abs/2205.12253 (2022) - [i62]Shariq Iqbal, Robby Costales, Fei Sha:
ALMA: Hierarchical Learning for Composite Multi-Agent Tasks. CoRR abs/2205.14205 (2022) - [i61]Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha:
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing. CoRR abs/2209.14899 (2022) - [i60]Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, William W. Cohen:
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference. CoRR abs/2212.08153 (2022) - 2021
- [c103]Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
When MAML Can Adapt Fast and How to Assist When It Cannot. AISTATS 2021: 244-252 - [c102]Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein:
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections. EACL 2021: 2540-2549 - [c101]Bowen Zhang, Hexiang Hu, Linlu Qiu, Peter Shaw, Fei Sha:
Visually Grounded Concept Composition. EMNLP (Findings) 2021: 201-215 - [c100]Linlu Qiu, Hexiang Hu, Bowen Zhang, Peter Shaw, Fei Sha:
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? EMNLP (1) 2021: 2180-2188 - [c99]Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, Eugene Ie:
CoMSum and SIBERT: A Dataset and Neural Model for Query-Based Multi-document Summarization. ICDAR (2) 2021: 84-98 - [c98]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, Fei Sha:
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning. ICML 2021: 4596-4606 - [c97]Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, Fei Sha:
ReadTwice: Reading Very Large Documents with Memories. NAACL-HLT 2021: 5189-5195 - [i59]Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein:
DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections. CoRR abs/2102.13247 (2021) - [i58]Sébastien M. R. Arnold, Fei Sha:
Embedding Adaptation is Still Needed for Few-Shot Learning. CoRR abs/2104.07255 (2021) - [i57]Yury Zemlyanskiy, Joshua Ainslie, Michiel de Jong, Philip Pham, Ilya Eckstein, Fei Sha:
ReadTwice: Reading Very Large Documents with Memories. CoRR abs/2105.04241 (2021) - [i56]Linlu Qiu, Hexiang Hu, Bowen Zhang, Peter Shaw, Fei Sha:
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? CoRR abs/2109.12243 (2021) - [i55]Bowen Zhang, Hexiang Hu, Linlu Qiu, Peter Shaw, Fei Sha:
Visually Grounded Concept Composition. CoRR abs/2109.14115 (2021) - [i54]Michiel de Jong, Yury Zemlyanskiy, Nicholas FitzGerald, Fei Sha, William W. Cohen:
Mention Memory: incorporating textual knowledge into Transformers through entity mention attention. CoRR abs/2110.06176 (2021) - [i53]Filipe de Avila Belbute-Peres, Yi-Fan Chen, Fei Sha:
HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks. CoRR abs/2111.01008 (2021) - [i52]Wang Zhu, Peter Shaw, Tal Linzen, Fei Sha:
Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks. CoRR abs/2111.05013 (2021) - [i51]Bowen Zhang, Jiahui Yu, Christopher Fifty, Wei Han, Andrew M. Dai, Ruoming Pang, Fei Sha:
Co-training Transformer with Videos and Images Improves Action Recognition. CoRR abs/2112.07175 (2021) - [i50]Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Krzysztof Nowak, Tal Linzen, Fei Sha, Kristina Toutanova:
Improving Compositional Generalization with Latent Structure and Data Augmentation. CoRR abs/2112.07610 (2021) - 2020
- [j13]Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha:
Classifier and Exemplar Synthesis for Zero-Shot Learning. Int. J. Comput. Vis. 128(1): 166-201 (2020) - [c96]Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha:
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps. ACL 2020: 2539-2556 - [c95]Yiming Yan, Melissa Ailem, Fei Sha:
Amortized Inference of Variational Bounds for Learning Noisy-OR. AISTATS 2020: 3632-3641 - [c94]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions. CVPR 2020: 8805-8814 - [c93]Bowen Zhang, Hexiang Hu, Vihan Jain, Eugene Ie, Fei Sha:
Learning to Represent Image and Text with Denotation Graph. EMNLP (1) 2020: 823-839 - [i49]Bowen Zhang, Hexiang Hu, Fei Sha:
Visual Storytelling via Predicting Anchor Word Embeddings in the Stories. CoRR abs/2001.04541 (2020) - [i48]Wang Zhu, Hexiang Hu, Jiacheng Chen, Zhiwei Deng, Vihan Jain, Eugene Ie, Fei Sha:
BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps. CoRR abs/2005.04625 (2020) - [i47]Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Böhmer, Shimon Whiteson, Fei Sha:
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning. CoRR abs/2006.04222 (2020) - [i46]Zhiyun Lu, Eugene Ie, Fei Sha:
Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation. CoRR abs/2006.07584 (2020) - [i45]Hexiang Hu, Ozan Sener, Fei Sha, Vladlen Koltun:
Drinking from a Firehose: Continual Learning with Web-scale Natural Language. CoRR abs/2007.09335 (2020) - [i44]Bowen Zhang, Hexiang Hu, Vihan Jain, Eugene Ie, Fei Sha:
Learning to Represent Image and Text with Denotation Graph. CoRR abs/2010.02949 (2020) - [i43]Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha, Eugene Ie:
AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization. CoRR abs/2010.12694 (2020) - [i42]Bowen Zhang, Hexiang Hu, Joonseok Lee, Ming Zhao, Sheide Chammas, Vihan Jain, Eugene Ie, Fei Sha:
A Hierarchical Multi-Modal Encoder for Moment Localization in Video Corpus. CoRR abs/2011.09046 (2020)
2010 – 2019
- 2019
- [j12]Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei Sha:
Kernel Approximation Methods for Speech Recognition. J. Mach. Learn. Res. 20: 59:1-59:36 (2019) - [c92]Jin Joo Lee, Fei Sha, Cynthia Breazeal:
A Bayesian Theory of Mind Approach to Nonverbal Communication. HRI 2019: 487-496 - [c91]Shariq Iqbal, Fei Sha:
Actor-Attention-Critic for Multi-Agent Reinforcement Learning. ICML 2019: 2961-2970 - [c90]Zhiyun Lu, Liyu Chen, Chao-Kai Chiang, Fei Sha:
Hyper-parameter Tuning under a Budget Constraint. IJCAI 2019: 5744-5750 - [i41]Zhiyun Lu, Chao-Kai Chiang, Fei Sha:
Hyper-parameter Tuning under a Budget Constraint. CoRR abs/1902.00532 (2019) - [i40]Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha:
Synthesized Policies for Transfer and Adaptation across Tasks and Environments. CoRR abs/1904.03276 (2019) - [i39]Shariq Iqbal, Fei Sha:
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning. CoRR abs/1905.12127 (2019) - [i38]Yiming Yan, Melissa Ailem, Fei Sha:
Amortized Inference of Variational Bounds for Learning Noisy-OR. CoRR abs/1906.02428 (2019) - [i37]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Classifier Synthesis for Generalized Few-Shot Learning. CoRR abs/1906.02944 (2019) - [i36]Michiel de Jong, Fei Sha:
Neural Theorem Provers Do Not Learn Rules Without Exploration. CoRR abs/1906.06805 (2019) - [i35]Melissa Ailem, Bowen Zhang, Fei Sha:
Topic Augmented Generator for Abstractive Summarization. CoRR abs/1908.07026 (2019) - [i34]Sébastien M. R. Arnold, Shariq Iqbal, Fei Sha:
Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning. CoRR abs/1910.13603 (2019) - 2018
- [j11]Tarek F. Abdelzaher, Nora Ayanian, Tamer Basar, Suhas N. Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrède Lepoint, Benjamin M. Marlin, Klara Nahrstedt, David M. Nicol, Raj Rajkumar, Stephen Russell, Sanjit A. Seshia, Fei Sha, Prashant J. Shenoy, Mani B. Srivastava, Gaurav S. Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin H. Vaidya, Venugopal V. Veeravalli:
Toward an Internet of Battlefield Things: A Resilience Perspective. Computer 51(11): 24-36 (2018) - [c89]Jan Kremer, Fei Sha, Christian Igel:
Robust Active Label Correction. AISTATS 2018: 308-316 - [c88]Soravit Changpinyo, Hexiang Hu, Fei Sha:
Multi-Task Learning for Sequence Tagging: An Empirical Study. COLING 2018: 2965-2977 - [c87]Yury Zemlyanskiy, Fei Sha:
Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner. CoNLL 2018: 551-561 - [c86]Hexiang Hu, Wei-Lun Chao, Fei Sha:
Learning Answer Embeddings for Visual Question Answering. CVPR 2018: 5428-5436 - [c85]Wei-Lun Chao, Hexiang Hu, Fei Sha:
Cross-Dataset Adaptation for Visual Question Answering. CVPR 2018: 5716-5725 - [c84]Bowen Zhang, Hexiang Hu, Fei Sha:
Cross-Modal and Hierarchical Modeling of Video and Text. ECCV (13) 2018: 385-401 - [c83]Ke Zhang, Kristen Grauman, Fei Sha:
Retrospective Encoders for Video Summarization. ECCV (8) 2018: 391-408 - [c82]Melissa Ailem, Bowen Zhang, Aurélien Bellet, Pascal Denis, Fei Sha:
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images. EMNLP 2018: 1478-1487 - [c81]Tarek F. Abdelzaher, Nora Ayanian, Tamer Basar, Suhas N. Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrède Lepoint, Benjamin M. Marlin, Klara Nahrstedt, David M. Nicol, Raj Rajkumar, Stephen Russell, Sanjit A. Seshia, Fei Sha, Prashant J. Shenoy, Mani B. Srivastava, Gaurav S. Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin H. Vaidya, Venugopal V. Veeravalli:
Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT. ICDCS 2018: 1129-1138 - [c80]Wei-Lun Chao, Hexiang Hu, Fei Sha:
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets. NAACL-HLT 2018: 431-441 - [c79]Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha:
Synthesize Policies for Transfer and Adaptation across Tasks and Environments. NeurIPS 2018: 1176-1185 - [i33]Hexiang Hu, Wei-Lun Chao, Fei Sha:
Learning Answer Embeddings for Visual Question Answering. CoRR abs/1806.03724 (2018) - [i32]Wei-Lun Chao, Hexiang Hu, Fei Sha:
Cross-Dataset Adaptation for Visual Question Answering. CoRR abs/1806.03726 (2018) - [i31]Soravit Changpinyo, Hexiang Hu, Fei Sha:
Multi-Task Learning for Sequence Tagging: An Empirical Study. CoRR abs/1808.04151 (2018) - [i30]Yury Zemlyanskiy, Fei Sha:
Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner. CoRR abs/1808.07104 (2018) - [i29]Shariq Iqbal, Fei Sha:
Actor-Attention-Critic for Multi-Agent Reinforcement Learning. CoRR abs/1810.02912 (2018) - [i28]Bowen Zhang, Hexiang Hu, Fei Sha:
Cross-Modal and Hierarchical Modeling of Video and Text. CoRR abs/1810.07212 (2018) - [i27]Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei Sha:
Learning Embedding Adaptation for Few-Shot Learning. CoRR abs/1812.03664 (2018) - [i26]Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha:
Classifier and Exemplar Synthesis for Zero-Shot Learning. CoRR abs/1812.06423 (2018) - 2017
- [j10]Kun Fu, Junqi Jin, Runpeng Cui, Fei Sha, Changshui Zhang:
Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts. IEEE Trans. Pattern Anal. Mach. Intell. 39(12): 2321-2334 (2017) - [c78]Chenxi Liu, Junhua Mao, Fei Sha, Alan L. Yuille:
Attention Correctness in Neural Image Captioning. AAAI 2017: 4176-4182 - [c77]Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha:
FastMask: Segment Multi-scale Object Candidates in One Shot. CVPR 2017: 2280-2288 - [c76]Soravit Changpinyo, Wei-Lun Chao, Fei Sha:
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning. ICCV 2017: 3496-3505 - [c75]Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei Sha:
An Empirical Study on The Properties of Random Bases for Kernel Methods. NIPS 2017: 2763-2774 - [p2]Boqing Gong, Kristen Grauman, Fei Sha:
Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation. Domain Adaptation in Computer Vision Applications 2017: 59-79 - [i25]Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel J. Hsu, Brian Kingsbury, Michael Picheny, Fei Sha:
Kernel Approximation Methods for Speech Recognition. CoRR abs/1701.03577 (2017) - [i24]Hexiang Hu, Zhiwei Deng, Guang-Tong Zhou, Fei Sha, Greg Mori:
LabelBank: Revisiting Global Perspectives for Semantic Segmentation. CoRR abs/1703.09891 (2017) - [i23]Wei-Lun Chao, Hexiang Hu, Fei Sha:
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets. CoRR abs/1704.07121 (2017) - 2016
- [j9]Christian Potthast, Andreas Breitenmoser, Fei Sha, Gaurav S. Sukhatme:
Active multi-view object recognition: A unifying view on online feature selection and view planning. Robotics Auton. Syst. 84: 31-47 (2016) - [c74]Ying Li, Fei Sha, Shujuan Wang, Tao Hu:
The improvement of page sorting algorithm for music users in Nutch. ICIS 2016: 1-4 - [c73]Yuan Shi, Wenzhe Li, Fei Sha:
Metric Learning for Ordinal Data. AAAI 2016: 2030-2036 - [c72]Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman:
Summary Transfer: Exemplar-Based Subset Selection for Video Summarization. CVPR 2016: 1059-1067 - [c71]Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha:
Synthesized Classifiers for Zero-Shot Learning. CVPR 2016: 5327-5336 - [c70]Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha:
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild. ECCV (2) 2016: 52-68 - [c69]Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman:
Video Summarization with Long Short-Term Memory. ECCV (7) 2016: 766-782 - [c68]Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
A comparison between deep neural nets and kernel acoustic models for speech recognition. ICASSP 2016: 5070-5074 - [c67]Gao Huang, Chuan Guo, Matt J. Kusner, Yu Sun, Fei Sha, Kilian Q. Weinberger:
Supervised Word Mover's Distance. NIPS 2016: 4862-4870 - [i22]Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha:
Synthesized Classifiers for Zero-Shot Learning. CoRR abs/1603.00550 (2016) - [i21]Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman:
Summary Transfer: Exemplar-based Subset Selection for Video Summarization. CoRR abs/1603.03369 (2016) - [i20]Zhiyun Lu, Dong Guo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition. CoRR abs/1603.05800 (2016) - [i19]Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha:
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild. CoRR abs/1605.04253 (2016) - [i18]Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen Grauman:
Video Summarization with Long Short-term Memory. CoRR abs/1605.08110 (2016) - [i17]Soravit Changpinyo, Wei-Lun Chao, Fei Sha:
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning. CoRR abs/1605.08151 (2016) - [i16]Chenxi Liu, Junhua Mao, Fei Sha, Alan L. Yuille:
Attention Correctness in Neural Image Captioning. CoRR abs/1605.09553 (2016) - [i15]Hexiang Hu, Zhiwei Deng, Guang-Tong Zhou, Fei Sha, Greg Mori:
Recalling Holistic Information for Semantic Segmentation. CoRR abs/1611.08061 (2016) - [i14]Nan Ding, Sebastian Goodman, Fei Sha, Radu Soricut:
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task. CoRR abs/1612.07833 (2016) - [i13]Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha:
FastMask: Segment Object Multi-scale Candidates in One Shot. CoRR abs/1612.08843 (2016) - 2015
- [j8]Minmin Chen, Kilian Q. Weinberger, Zhixiang Eddie Xu, Fei Sha:
Marginalizing stacked linear denoising autoencoders. J. Mach. Learn. Res. 16: 3849-3875 (2015) - [c66]Kuan Liu, Aurélien Bellet, Fei Sha:
Similarity Learning for High-Dimensional Sparse Data. AISTATS 2015 - [c65]Caitlyn Clabaugh, Gisele Ragusa, Fei Sha, Maja J. Mataric:
Designing a socially assistive robot for personalized number concepts learning in preschool children. ICDL-EPIROB 2015: 314-319 - [c64]Wei-Lun Chao, Justin Solomon, Dominik L. Michels, Fei Sha:
Exponential Integration for Hamiltonian Monte Carlo. ICML 2015: 1142-1151 - [c63]Christian Potthast, Andreas Breitenmoser, Fei Sha, Gaurav S. Sukhatme:
Active Multi-view Object Recognition and Online Feature Selection. ISRR (2) 2015: 471-488 - [c62]Aurélien Bellet, Yingyu Liang, Alireza Bagheri Garakani, Maria-Florina Balcan, Fei Sha:
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning. SDM 2015: 478-486 - [c61]Wei-Lun Chao, Boqing Gong, Kristen Grauman, Fei Sha:
Large-Margin Determinantal Point Processes. UAI 2015: 191-200 - [i12]Junqi Jin, Kun Fu, Runpeng Cui, Fei Sha, Changshui Zhang:
Aligning where to see and what to tell: image caption with region-based attention and scene factorization. CoRR abs/1506.06272 (2015) - 2014
- [j7]Boqing Gong, Kristen Grauman, Fei Sha:
Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition. Int. J. Comput. Vis. 109(1-2): 3-27 (2014) - [c60]Yuan Shi, Aurélien Bellet, Fei Sha:
Sparse Compositional Metric Learning. AAAI 2014: 2078-2084 - [c59]Dinesh Jayaraman, Fei Sha, Kristen Grauman:
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share. CVPR 2014: 1629-1636 - [c58]Zi Wang, Fei Sha:
Discriminative non-negative matrix factorization for single-channel speech separation. ICASSP 2014: 3749-3753 - [c57]Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. ICML 2014: 19-27 - [c56]Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. ICML 2014: 370-378 - [c55]Minmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio:
Marginalized Denoising Auto-encoders for Nonlinear Representations. ICML 2014: 1476-1484 - [c54]Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha:
Diverse Sequential Subset Selection for Supervised Video Summarization. NIPS 2014: 2069-2077 - [i11]Aurélien Bellet, Yingyu Liang, Alireza Bagheri Garakani, Maria-Florina Balcan, Fei Sha:
Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning. CoRR abs/1404.2644 (2014) - [i10]Yuan Shi, Aurélien Bellet, Fei Sha:
Sparse Compositional Metric Learning. CoRR abs/1404.4105 (2014) - [i9]Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis:
Two-Stage Metric Learning. CoRR abs/1405.2798 (2014) - [i8]Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha:
Large-Margin Determinantal Point Processes. CoRR abs/1411.1537 (2014) - [i7]Kuan Liu, Aurélien Bellet, Fei Sha:
Similarity Learning for High-Dimensional Sparse Data. CoRR abs/1411.2374 (2014) - [i6]Zhiyun Lu, Avner May, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei Sha:
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets. CoRR abs/1411.4000 (2014) - 2013
- [c53]Jaechul Kim, Ce Liu, Fei Sha, Kristen Grauman:
Deformable Spatial Pyramid Matching for Fast Dense Correspondences. CVPR 2013: 2307-2314 - [c52]Boqing Gong, Kristen Grauman, Fei Sha:
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation. ICML (1) 2013: 222-230 - [c51]Sung Ju Hwang, Kristen Grauman, Fei Sha:
Analogy-preserving Semantic Embedding for Visual Object Categorization. ICML (3) 2013: 639-647 - [c50]Boqing Gong, Kristen Grauman, Fei Sha:
Reshaping Visual Datasets for Domain Adaptation. NIPS 2013: 1286-1294 - [c49]Soravit Changpinyo, Kuan Liu, Fei Sha:
Similarity Component Analysis. NIPS 2013: 1511-1519 - [i5]Zhixiang Eddie Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha:
An alternative text representation to TF-IDF and Bag-of-Words. CoRR abs/1301.6770 (2013) - [i4]Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. CoRR abs/1310.4210 (2013) - 2012
- [c48]Tomer Levinboim, Fei Sha:
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping. AAAI 2012: 984-990 - [c47]Zhixiang Eddie Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha:
From sBoW to dCoT marginalized encoders for text representation. CIKM 2012: 1879-1884 - [c46]Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman:
Geodesic flow kernel for unsupervised domain adaptation. CVPR 2012: 2066-2073 - [c45]Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha:
Marginalized Denoising Autoencoders for Domain Adaptation. ICML 2012 - [c44]Yuan Shi, Fei Sha:
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation. ICML 2012 - [c43]Dingchao Lu, Fei Sha:
Predicting Likability of Speakers with Gaussian Processes. INTERSPEECH 2012: 286-289 - [c42]Sung Ju Hwang, Kristen Grauman, Fei Sha:
Semantic Kernel Forests from Multiple Taxonomies. NIPS 2012: 1727-1735 - [c41]Dor Kedem, Stephen Tyree, Kilian Q. Weinberger, Fei Sha, Gert R. G. Lanckriet:
Non-linear Metric Learning. NIPS 2012: 2582-2590 - [c40]Bin Liu, Yurong Jiang, Fei Sha, Ramesh Govindan:
Cloud-enabled privacy-preserving collaborative learning for mobile sensing. SenSys 2012: 57-70 - [i3]Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha:
Marginalized Denoising Autoencoders for Domain Adaptation. CoRR abs/1206.4683 (2012) - 2011
- [j6]Junping Zhang, Ben Tan, Fei Sha, Li He:
Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features. IEEE Trans. Intell. Transp. Syst. 12(4): 1037-1046 (2011) - [c39]Matthew E. Taylor, Brian Kulis, Fei Sha:
Metric learning for reinforcement learning agents. AAMAS 2011: 777-784 - [c38]Sung Ju Hwang, Fei Sha, Kristen Grauman:
Sharing features between objects and their attributes. CVPR 2011: 1761-1768 - [c37]Xu Li, Fei Sha:
Application of moment invariants in checking the overall dimension of the head cover. EMEIT 2011: 932-935 - [c36]Geoffrey Zweig, Patrick Nguyen, Dirk Van Compernolle, Kris Demuynck, Les E. Atlas, Pascal Clark, Gregory Sell, Meihong Wang, Fei Sha, Hynek Hermansky, Damianos G. Karakos, Aren Jansen, Samuel Thomas, Sivaram G. S. V. S., Samuel R. Bowman, Justine T. Kao:
Speech recognitionwith segmental conditional random fields: A summary of the JHU CLSP 2010 Summer Workshop. ICASSP 2011: 5044-5047 - [c35]Zhuoliang Kang, Kristen Grauman, Fei Sha:
Learning with Whom to Share in Multi-task Feature Learning. ICML 2011: 521-528 - [c34]Leslie Cheung, Leana Golubchik, Fei Sha:
A Study of Web Services Performance Prediction: A Client's Perspective. MASCOTS 2011: 75-84 - [c33]Lawrence K. Saul, Chih-Chieh Cheng, Fei Sha:
Online learning of large margin hidden Markov models for automatic speech recognition. MLSLP 2011 - [c32]Sung Ju Hwang, Kristen Grauman, Fei Sha:
Learning a Tree of Metrics with Disjoint Visual Features. NIPS 2011: 621-629 - [c31]Meihong Wang, Fei Sha:
Information Theoretical Clustering via Semidefinite Programming. AISTATS 2011: 761-769 - [i2]Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha:
Rapid Feature Learning with Stacked Linear Denoisers. CoRR abs/1105.0972 (2011) - [i1]Yuan Shi, Yung-Kyun Noh, Fei Sha, Daniel D. Lee:
Learning Discriminative Metrics via Generative Models and Kernel Learning. CoRR abs/1109.3940 (2011) - 2010
- [j5]Sriram Sankararaman, Fei Sha, Jack F. Kirsch, Michael I. Jordan, Kimmen Sjölander:
Active site prediction using evolutionary and structural information. Bioinform. 26(5): 617-624 (2010) - [j4]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models. IEEE J. Sel. Top. Signal Process. 4(6): 926-942 (2010) - [j3]Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]. IEEE Signal Process. Mag. 27(3): 146-158 (2010) - [c30]Yi Ma, Fei Sha, Lawrence Carin, Gilad Lerman, Neil D. Lawrence:
Invited Talk Abstracts. AAAI Fall Symposium: Manifold Learning and Its Applications 2010 - [c29]Meihong Wang, Fei Sha, Michael I. Jordan:
Unsupervised Kernel Dimension Reduction. NIPS 2010: 2379-2387 - [c28]Dian Gong, Fei Sha, Gérard G. Medioni:
Locally Linear Denoising on Image Manifolds. AISTATS 2010: 265-272
2000 – 2009
- 2009
- [c27]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Large-margin feature adaptation for automatic speech recognition. ASRU 2009: 87-92 - [c26]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
Matrix updates for perceptron training of continuous density hidden Markov models. ICML 2009: 153-160 - [c25]Chih-Chieh Cheng, Fei Sha, Lawrence K. Saul:
A fast online algorithm for large margin training of continuous density hidden Markov models. INTERSPEECH 2009: 668-671 - [c24]Nilesh N. Dalvi, Philip Bohannon, Fei Sha:
Robust web extraction: an approach based on a probabilistic tree-edit model. SIGMOD Conference 2009: 335-348 - 2008
- [c23]Simon Lacoste-Julien, Fei Sha, Michael I. Jordan:
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. NIPS 2008: 897-904 - 2007
- [j2]Fei Sha, Yuanqing Lin, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Nonnegative Quadratic Programming. Neural Comput. 19(8): 2004-2031 (2007) - [c22]Fei Sha, Lawrence K. Saul:
Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models. ICASSP (4) 2007: 313-316 - [c21]Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik:
Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. ICCV 2007: 1-8 - [c20]Jens Nilsson, Fei Sha, Michael I. Jordan:
Regression on manifolds using kernel dimension reduction. ICML 2007: 697-704 - [c19]Fei Sha, Y. Albert Park, Lawrence K. Saul:
Multiplicative Updates for L1-Regularized Linear and Logistic Regression. IDA 2007: 13-24 - 2006
- [c18]Fei Sha, Lawrence K. Saul:
Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition. ICASSP (1) 2006: 265-268 - [c17]Fei Sha, Lawrence K. Saul:
Large Margin Hidden Markov Models for Automatic Speech Recognition. NIPS 2006: 1249-1256 - [c16]Kilian Q. Weinberger, Fei Sha, Qihui Zhu, Lawrence K. Saul:
Graph Laplacian Regularization for Large-Scale Semidefinite Programming. NIPS 2006: 1489-1496 - [p1]Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee:
Spectral Methods for Dimensionality Reduction. Semi-Supervised Learning 2006: 292-308 - 2005
- [c15]Fei Sha, Lawrence K. Saul:
Analysis and extension of spectral methods for nonlinear dimensionality reduction. ICML 2005: 784-791 - 2004
- [c14]Fei Sha, John Ashley Burgoyne, Lawrence K. Saul:
Multiband statistical learning for f0 estimation in speech. ICASSP (5) 2004: 661-664 - [c13]Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul:
Learning a kernel matrix for nonlinear dimensionality reduction. ICML 2004 - [c12]Fei Sha, Lawrence K. Saul:
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization. NIPS 2004: 1233-1240 - 2003
- [c11]Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Large Margin Classifiers. COLT 2003: 188-202 - [c10]Lawrence K. Saul, Fei Sha, Daniel D. Lee:
Statistical signal processing with nonnegativity constraints. INTERSPEECH 2003: 1001-1004 - [c9]Fei Sha, Fernando C. N. Pereira:
Shallow Parsing with Conditional Random Fields. HLT-NAACL 2003 - 2002
- [c8]Fei Sha, Lawrence K. Saul, Daniel D. Lee:
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines. NIPS 2002: 1041-1048 - 2000
- [c7]Fei Sha, Georges Gardarin, Laurent Némirovski:
A Semi-Structured Data Cartridge for Relational Databases. ICDE 2000: 192
1990 – 1999
- 1999
- [c6]Fei Sha, Georges Gardarin, Laurent Némirovski:
Managing Semistructured Data in Object-Relational DBMS. Proc. 15èmes Journées Bases de Données Avancées, BDA 1999: 101-115 - [c5]Georges Gardarin, Fei Sha, Tuyet-Tram Dang-Ngoc:
XML-based Components for Federating Multiple Heterogeneous Data Sources. ER 1999: 506-519 - [c4]Luc Bouganim, Tatiana Chan-Sine-Ying, Tuyet-Tram Dang-Ngoc, Jean-Luc Darroux, Georges Gardarin, Fei Sha:
Miro Web: Integrating Multiple Data Sources through Semistructured Data Types. VLDB 1999: 750-753 - 1997
- [c3]Georges Gardarin, Fei Sha:
Using Conceptual Modeling and Intelligent Agents to Integrate Semi-structured Documents in Federated Databases. Conceptual Modeling 1997: 87-99 - 1996
- [c2]Georges Gardarin, Fei Sha, Zhao-Hui Tang:
Calibrating the Query Optimizer Cost Model of IRO-DB, an Object-Oriented Federated Database System. BDA 1996: 37-56 - [c1]Georges Gardarin, Fei Sha, Zhao-Hui Tang:
Calibrating the Query Optimizer Cost Model of IRO-DB, an Object-Oriented Federated Database System. VLDB 1996: 378-389 - 1992
- [j1]Tülin Atmaca, Guy Pujolle, Fei Sha:
Performance Analysis of an Access Control Strategy in Integrated Networks. Comput. Networks ISDN Syst. 24(5): 421-434 (1992)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-18 20:27 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
