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Fei Sha
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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 - [i78]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) - 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
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