Fei Sha

Leana Golubchik, Fei Sha, Leslie Cheung, 2011, 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems.

Large-margin feature adaptation for automatic speech recognition

Lawrence K. Saul, Fei Sha, Chih-Chieh Cheng, 2009, 2009 IEEE Workshop on Automatic Speech Recognition & Understanding.

Diverse Sequential Subset Selection for Supervised Video Summarization

Kristen Grauman, Wei-Lun Chao, Boqing Gong, 2014, NIPS.

Marginalizing stacked linear denoising autoencoders

Kilian Q. Weinberger, Minmin Chen, Fei Sha, 2015, J. Mach. Learn. Res..

Aligning where to see and what to tell: image caption with region-based attention and scene factorization pdf

Changshui Zhang, Kun Fu, Junqi Jin, 2015, ArXiv.

Summary Transfer: Exemplar-Based Subset Selection for Video Summarization

Ke Zhang, Kristen Grauman, Wei-Lun Chao, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning pdf

Maria-Florina Balcan, Yingyu Liang, Fei Sha, 2014, ArXiv.

Deformable Spatial Pyramid Matching for Fast Dense Correspondences

Ce Liu, Kristen Grauman, Fei Sha, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Fei Sha, Shariq Iqbal, Fei Sha, 2018, ICML.

Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models

Lawrence K. Saul, Fei Sha, L. Saul, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Active multi-view object recognition: A unifying view on online feature selection and view planning

Gaurav S. Sukhatme, Andreas Breitenmoser, Christian Potthast, 2016, Robotics Auton. Syst..

Learning the Kernel Matrix with Low-Rank Multiplicative Shaping

Fei Sha, Tomer Levinboim, 2012, AAAI.

Analysis and extension of spectral methods for nonlinear dimensionality reduction

Lawrence K. Saul, Fei Sha, L. Saul, 2005, ICML.

Learning a Tree of Metrics with Disjoint Visual Features

Kristen Grauman, Sung Ju Hwang, Fei Sha, 2011, NIPS.

Reshaping Visual Datasets for Domain Adaptation

Kristen Grauman, Boqing Gong, Fei Sha, 2013, NIPS.

Learning with Whom to Share in Multi-task Feature Learning

Kristen Grauman, Fei Sha, Zhuoliang Kang, 2011, ICML.

Marginalized Denoising Auto-encoders for Nonlinear Representations

Yoshua Bengio, Kilian Q. Weinberger, Minmin Chen, 2014, ICML.

FastMask: Segment Multi-scale Object Candidates in One Shot

Yuning Jiang, Hexiang Hu, Fei Sha, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Supervised Word Mover's Distance

Matt J. Kusner, Kilian Q. Weinberger, Yu Sun, 2016, NIPS.

Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning pdf

Fei Sha, Shariq Iqbal, Fei Sha, 2019, ArXiv.

Graph Laplacian Regularization for Large-Scale Semidefinite Programming

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2006, NIPS.

Similarity Component Analysis

Soravit Changpinyo, Fei Sha, Kuan Liu, 2013, NIPS.

Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines

Daniel D. Lee, Lawrence K. Saul, Fei Sha, 2002, NIPS.

Synthesized Classifiers for Zero-Shot Learning

Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization

Lawrence K. Saul, Fei Sha, L. Saul, 2004, NIPS.

Unsupervised Kernel Dimension Reduction

Michael I. Jordan, Meihong Wang, Fei Sha, 2010, NIPS.

Analogy-preserving Semantic Embedding for Visual Object Categorization

Kristen Grauman, Sung Ju Hwang, Fei Sha, 2013, ICML.

Learning a kernel matrix for nonlinear dimensionality reduction

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2004, ICML.

Regression on manifolds using kernel dimension reduction

Michael I. Jordan, Fei Sha, Jens Nilsson, 2007, ICML '07.

Learning Discriminative Metrics via Generative Models and Kernel Learning pdf

Daniel D. Lee, Yuan Shi, Fei Sha, 2011, ArXiv.

Multiplicative Updates for Nonnegative Quadratic Programming

Daniel D. Lee, Lawrence K. Saul, Yuanqing Lin, 2007, Neural Computation.

Learning Answer Embeddings for Visual Question Answering

Hexiang Hu, Wei-Lun Chao, Fei Sha, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Recalling Holistic Information for Semantic Segmentation pdf

Greg Mori, Hexiang Hu, Zhiwei Deng, 2016, ArXiv.

Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation

Yuan Shi, Fei Sha, Yuan Shi, 2012, ICML.

Multi-Task Learning for Sequence Tagging: An Empirical Study pdf

Hexiang Hu, Soravit Changpinyo, Fei Sha, 2018, COLING.

Locally Linear Denoising on Image Manifolds

Gérard G. Medioni, Dian Gong, Fei Sha, 2010, AISTATS.

Demystifying Information-Theoretic Clustering pdf

Fei Sha, Simon DeDeo, Aram Galstyan, 2013, ICML.

Amortized Inference of Variational Bounds for Learning Noisy-OR pdf

Yiming Yan, Melissa Ailem, Fei Sha, 2020, AISTATS.

Learning Classifier Synthesis for Generalized Few-Shot Learning pdf

De-Chuan Zhan, Han-Jia Ye, Hexiang Hu, 2019, ArXiv.

Attention Correctness in Neural Image Captioning pdf

Chenxi Liu, Alan L. Yuille, Fei Sha, 2016, AAAI.

Fei Sha, Aurélien Bellet, Kuan Liu, 2014, AISTATS.

Matrix updates for perceptron training of continuous density hidden Markov models

Lawrence K. Saul, Fei Sha, Chih-Chieh Cheng, 2009, ICML '09.

Kernel Approximation Methods for Speech Recognition

Brian Kingsbury, Michael Picheny, Michael Collins, 2017, J. Mach. Learn. Res..

Active Multi-view Object Recognition and Online Feature Selection

Gaurav S. Sukhatme, Andreas Breitenmoser, Christian Potthast, 2015, ISRR.

The improvement of page sorting algorithm for music users in Nutch

Ying Li, Tao Hu, Shujuan Wang, 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

A Bayesian Theory of Mind Approach to Nonverbal Communication

Cynthia Breazeal, Jin Joo Lee, Fei Sha, 2019, 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

Statistical signal processing with nonnegativity constraints

Daniel D. Lee, Lawrence K. Saul, Fei Sha, 2003, INTERSPEECH.

Sharing features between objects and their attributes

Kristen Grauman, Sung Ju Hwang, Fei Sha, 2011, CVPR 2011.

Geodesic flow kernel for unsupervised domain adaptation

Yuan Shi, Kristen Grauman, Boqing Gong, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

An alternative text representation to TF-IDF and Bag-of-Words pdf

Kilian Q. Weinberger, Minmin Chen, Fei Sha, 2013, ArXiv.

Retrospective Encoders for Video Summarization

Ke Zhang, Kristen Grauman, Fei Sha, 2018, ECCV.

Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition

Lawrence K. Saul, Fei Sha, L. Saul, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

Kristen Grauman, Boqing Gong, Fei Sha, 2013, ICML.

A Semi-Structured Data Cartridge for Relational Databases

Georges Gardarin, Fei Sha, Laurent Némirovski, 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets pdf

Brian Kingsbury, Michael Picheny, Michael Collins, 2014, ArXiv.

Neural Theorem Provers Do Not Learn Rules Without Exploration pdf

Fei Sha, Michiel de Jong, Fei Sha, 2019, ArXiv.

Application of moment invariants in checking the overall dimension of the head cover

Xu Li, Fei Sha, 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

Information Theoretical Clustering via Semidefinite Programming

Meihong Wang, Fei Sha, Fei Sha, 2011, AISTATS.

A fast online algorithm for large margin training of continuous density hidden Markov models

Lawrence K. Saul, Fei Sha, Chih-Chieh Cheng, 2009, INTERSPEECH.

Marginalized Denoising Autoencoders for Domain Adaptation pdf

Kilian Q. Weinberger, Minmin Chen, Fei Sha, 2012, ICML.

Multiband statistical learning for f/sub 0/ estimation in speech

Lawrence K. Saul, John Ashley Burgoyne, Fei Sha, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Metric Learning for Ordinal Data

Wenzhe Li, Yuan Shi, Fei Sha, 2016, AAAI.

Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models

Lawrence K. Saul, Fei Sha, Chih-Chieh Cheng, 2010, IEEE Journal of Selected Topics in Signal Processing.

Cross-Dataset Adaptation for Visual Question Answering

Hexiang Hu, Wei-Lun Chao, Fei Sha, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Discriminative non-negative matrix factorization for single-channel speech separation

Zi Wang, Fei Sha, Fei Sha, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Rapid Feature Learning with Stacked Linear Denoisers pdf

Kilian Q. Weinberger, Fei Sha, Zhixiang Eddie Xu, 2011, ArXiv.

FastMask: Segment Object Multi-scale Candidates in One Shot pdf

Yuning Jiang, Hexiang Hu, Fei Sha, 2016, ArXiv.

Video Summarization with Long Short-Term Memory pdf

Ke Zhang, Kristen Grauman, Wei-Lun Chao, 2016, ECCV.

A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images

Bowen Zhang, Pascal Denis, Melissa Ailem, 2018, EMNLP.

Cross-Modal and Hierarchical Modeling of Video and Text

Bowen Zhang, Hexiang Hu, Fei Sha, 2018, ECCV.

Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

Fei Sha, Yury Zemlyanskiy, Fei Sha, 2018, CoNLL.

Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

Wei-Lun Chao, Soravit Changpinyo, Fei Sha, 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

Maria-Florina Balcan, Yingyu Liang, Fei Sha, 2014, SDM.

Sparse Compositional Metric Learning pdf

Yuan Shi, Fei Sha, Aurélien Bellet, 2014, AAAI.

Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition

Kristen Grauman, Boqing Gong, Fei Sha, 2014, International Journal of Computer Vision.

Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets pdf

Hexiang Hu, Wei-Lun Chao, Fei Sha, 2017, NAACL.

Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]

Kilian Q. Weinberger, Lawrence K. Saul, Fei Sha, 2010, IEEE Signal Processing Magazine.

Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification

Jitendra Malik, Yoram Singer, Fei Sha, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Exponential Integration for Hamiltonian Monte Carlo

Justin Solomon, Wei-Lun Chao, Fei Sha, 2015, ICML.

Classifier and Exemplar Synthesis for Zero-Shot Learning

Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, 2018, International Journal of Computer Vision.

Invited Talk Abstracts

Neil D. Lawrence, Yi Ma, Lawrence Carin, 2010, AAAI Fall Symposium: Manifold Learning and Its Applications.

Using Conceptual Modeling and Intelligent Agents to Integrate Semi-structured Documents in Federated Databases

Georges Gardarin, Fei Sha, 1997, Conceptual Modeling.

Designing a socially assistive robot for personalized number concepts learning in preschool children

Maja J. Mataric, Gisele Ragusa, Fei Sha, 2015, 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob).

Learning to Generalize Compositionally by Transferring Across Semantic Parsing Tasks pdf

Tal Linzen, Fei Sha, Peter Shaw, 2021, ArXiv.

Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning

De-Chuan Zhan, Hexiang Hu, Fei Sha, 2019, International Journal of Computer Vision.

Improving Compositional Generalization with Latent Structure and Data Augmentation pdf

Kristina Toutanova, Fei Sha, Peter Shaw, 2021, ArXiv.

DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections pdf

Fei Sha, Ruining He, Sudeep Gandhe, 2021, Conference of the European Chapter of the Association for Computational Linguistics.

Embedding Adaptation is Still Needed for Few-Shot Learning pdf

Fei Sha, Sébastien M. R. Arnold, S'ebastien M. R. Arnold, 2021, ArXiv.

Co-training Transformer with Videos and Images Improves Action Recognition pdf

Andrew M. Dai, Fei Sha, Ruoming Pang, 2021, ArXiv.