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- affiliation: University of Southern California, Los Angeles, USA
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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 - [i83]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) - [i82]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) - [i81]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) - [i80]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) - [i79]Ignacio Lopez-Gomez, Zhong Yi Wan, Leonardo Zepeda-Núñez, Tapio Schneider, John R. Anderson, Fei Sha:
Dynamical-generative downscaling of climate model ensembles. CoRR abs/2410.01776 (2024) - [i78]Xuchan Bao, Judith Yue Li, Zhong Yi Wan, Kun Su, Timo I. Denk, Joonseok Lee, Dima Kuzmin, Fei Sha:
Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval with Semantic Guidance. CoRR abs/2412.04746 (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]