Learning Embedding Adaptation for Few-Shot Learning Permalink
Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, Fei ShaarXiv Technical Report, 2018.
Publications
2018
Classifier and Exemplar Synthesis for Zero-Shot Learning Permalink
Soravit Changpinyo*, Wei-Lun Chao*, Boqing Gong, Fei ShaInternational Journal of Computer Vision (IJCV), 2018.
Synthesize Policies for Transfer and Adaptation across Tasks and Environments Permalink
Hexiang Hu*, Liyu Chen*, Fei ShaProc. Neural Information Processing Systems (NeurIPS), Spotlight Presentation, 2018.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images Permalink
Melissa Ailem, Bowen Zhang, Aurélien Bellet, Pascal Denis, Fei ShaProc. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
Cross-Modal and Hierarchical Modeling of Video and Text Permalink
Bowen Zhang*, Hexiang Hu*, Fei ShaProc. European Conference on Computer Vision (ECCV), 2018.
Retrospective Encoders for Video Summarization Permalink
Ke Zhang, Kristen Grauman, Fei ShaProc. European Conference on Computer Vision (ECCV), 2018.
Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner Permalink
Yury Zemlyanskiy, Fei ShaProc. Conference on Natural Language Learning (CoNLL), 2018.
Multi-Task Learning for Sequence Tagging: An Empirical Study Permalink
Soravit Changpinyo, Hexiang Hu, Fei ShaProc. International Conference on Computational Linguistics (COLING), 2018.
Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT Permalink
Tarek Abdelzaher, Nora Ayanian, Tamer Basar, Suhas Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrede Lepoint, Ben Marlin, Klara Nahrstedt, David Nicol, Raj Rajkumar, Stephen Russell, Sanjit Seshia, Fei Sha, Prashant Shenoy, Mani Srivastava, Gaurav Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin Vaidya, Venu VeeravalliProc. International Conference on Distributed Computing Systems (ICDCS), 2018.
Learning Answer Embeddings for Visual Question Answering Permalink
Wei-Lun Chao*, Hexiang Hu*, Fei ShaProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Cross-Dataset Adaptation for Visual Question Answering Permalink
Wei-Lun Chao*, Hexiang Hu*, Fei ShaProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets Permalink
Wei-Lun Chao*, Hexiang Hu*, and Fei ShaProc. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Oral Presentation, 2018.
Active Multi-view Object Recognition and Online Feature Selection Permalink
Christian Potthast, Andreas Breitenmoser, Fei Sha, Gaurav S SukhatmeRobotics Research, 2018.
2017
An Empirical Study on The Properties of Random Bases for Kernel Methods Permalink
Maximilian Alber, Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Fei ShaProc. Conference on Neural Information Processing Systems (NIPS), 2017.
Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts Permalink
Kun Fu, Junqi Jin, Runpeng Cui, Fei Sha, Changshui ZhangIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2017.
Geodesic Flow Kernel and Landmarks: Kernel Methods for Unsupervised Domain Adaptation Permalink
Boqing Gong, Kristen Grauman, Fei ShaDomain Adaptation in Computer Vision Applications, 2017.
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning Permalink
Soravit Changpinyo*, Wei-Lun Chao*, Fei ShaProc. International Conference on Computer Vision (ICCV), 2017.
FastMask: Segment Multi-scale Object Candidates in One Shot Permalink
Hexiang Hu*, Shiyi Lan*, Yuning Jiang, Zhimin Cao, Fei ShaProc. Conference on Computer Vision and Pattern Recognition (CVPR), Spotlight Presentation, 2017.
LabelBank: Revisiting Global Perspectives for Semantic Segmentation Permalink
Hexiang Hu*, Zhiwei Deng*, Guang-Tong Zhou, Fei Sha, Greg MoriarXiv Technical Report, 2017.
Divide, Share, and Conquer: Multi-task Attribute Learning with Selective Sharing Permalink
Chao-Yeh Chen*, Dinesh Jayaraman*, Fei Sha, Kristen GraumanVisual Attributes, 2017.
Attention Correctness in Neural Image Captioning Permalink
Chenxi Liu, Junhua Mao, Fei Sha, Alan L YuilleProc. AAAI Conference on Artificial Intelligence (AAAI), 2017.
Kernel Approximation Methods for Speech Recognition Permalink
Avner May, Alireza Bagheri Garakani, Zhiyun Lu, Dong Guo, Kuan Liu, Aurélien Bellet, Linxi Fan, Michael Collins, Daniel Hsu, Brian Kingsbury, Michael Picheny, Fei ShaarXiv Technical Report, 2017.
2016
Supervised Word Mover's Distance Permalink
Gao Huang*, Chuan Guo*, Matt J Kusner, Yu Sun, Fei Sha, Kilian Q WeinbergerProc. Neural Information Processing Systems (NIPS), 2016.
Recalling Holistic Information for Semantic Segmentation Permalink
Hexiang Hu*, Zhiwei Deng*, Guang-Tong Zhou, Fei Sha, Greg MoriarXiv Technical Report, 2016.
Active multi-view object recognition: A unifying view on online feature selection and view planning Permalink
Christian Potthast, Andreas Breitenmoser, Fei Sha, Gaurav S SukhatmeRobotics and Autonomous Systems, 2016.
Video Summarization with Long Short-term Memory Permalink
Ke Zhang, Fei ShaProc. European Conference on Computer Vision (ECCV), 2016.
Synthesized Classifiers for Zero-Shot Learning Permalink
Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei ShaProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild Permalink
Wei-Lun Chao*, Soravit Changpinyo*, Boqing Gong, Fei ShaProc. European Conference on Computer Vision (ECCV), Spotlight Presentation, 2016.
Summary Transfer: Exemplar-based Subset Selection for Video Summarization Permalink
Ke Zhang, Wei-Lun Chao, Fei Sha, Kristen GraumanProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition Permalink
Zhiyun Lu, Dong Quo, Alireza Bagheri Garakani, Kuan Liu, Avner May, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei ShaProc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
Metric Learning for Ordinal Data Permalink
Yuan Shi*, Wenzhe Li*, Fei ShaProc. AAAI Conference on Artificial Intelligence (AAAI), 2016.
2015
Designing a Socially Assistive Robot for Personalized Number Concepts Learning in Preschool Children Permalink
Caitlyn Clabaugh, Gisele Ragusa, Fei Sha, Maja MatarićProc. International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2016.
Large-Margin Determinantal Point Processes Permalink
Wei-Lun Chao*, Boqing Gong*, Kristen Grauman, Fei ShaProc. Conference on Uncertainty in Artificial Intelligence (UAI), 2015.
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning Permalink
Aurélien Bellet, Yingyu Liang, Alireza Bagheri Garakani, Maria-Florina Balcan, Fei ShaProc. SIAM International Conference on Data Mining (SDM), 2015.
Aligning where to see and what to tell: image caption with region-based attention and scene factorization Permalink
Junqi Jin, Kun Fu, Runpeng Cui, Fei Sha, Changshui ZhangarXiv Technical Report, 2015.
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets Permalink
Zhiyun Lu*, Avner May*, Kuan Liu, Alireza Bagheri Garakani, Dong Guo, Aurélien Bellet, Linxi Fan, Michael Collins, Brian Kingsbury, Michael Picheny, Fei ShaarXiv Technical Report, 2015.
Exponential Integration for Hamiltonian Monte Carlo Permalink
Wei-Lun Chao, Justin Solomon, Dominik Michels, Fei ShaProc. International Conference on Machine Learning (ICML), 2015.
Towards a Personalized Model of Number Concepts Learning in Preschool Children Permalink
Caitlyn Clabaugh, Fei Sha, Gisele Ragusa, Maja MataricProc. ICRA Workshop on Machine Learning for Social Robotics, 2015.
Similarity Learning for High-Dimensional Sparse Data Permalink
Kuan Liu, Aurélien Bellet, Fei ShaProc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.
Marginalizing Stacked Linear Denoising Autoencoders Permalink
Minmin Chen, Kilian Q Weinberger, Zhixiang Xu, Fei ShaJournal of Machine Learning Research, 2015.
2014
Diverse Sequential Subset Selection for Supervised Video Summarization Permalink
Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei ShaProc. Conference on Neural Information Processing Systems (NIPS), 2014.
Towards Interactive Object Recognition Permalink
Karol Hausman, Chet Corcos, Jorg Muller, Fei Sha, Gaurav S SukhatmeIROS Workshop on Robots in Clutter: Perception and Interaction in Clutter, 2014.
Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition Permalink
Boqing Gong, Kristen Grauman, Fei ShaInternational Journal of Computer Vision (IJCV), 2014.
Sparse Compositional Metric Learning Permalink
Yuan Shi*, Aurélien Bellet*, Fei ShaProc. AAAI Conference on Artificial Intelligence (AAAI), 2014.
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share Permalink
Dinesh Jayaraman, Fei Sha, Kristen GraumanProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
Distributed Frank-Wolfe Algorithm: A Unified Framework for Communication-Efficient Sparse Learning Permalink
Aurélien Bellet*, Yingyu Liang*, Alireza Bagheri Garakani, Maria-Florina Balcan, Fei ShaCoRR Technical Report, 2014.
Discriminative non-negative matrix factorization for single-channel speech separation Permalink
Zi Wang, Fei ShaProc. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
Two-Stage Metric Learning Permalink
Jun Wang, Ke Sun, Fei Sha, Stephane Marchand-Maillet, Alexandros KalousisProc. International Conference on Machine Learning (ICML), 2014.
Marginalized Denoising Auto-encoders for Nonlinear Representations Permalink
Minmin Chen, Kilian Weinberger, Fei Sha, Yoshua BengioProc. International Conference on Machine Learning (ICML), 2014.
Demystifying Information-Theoretic Clustering Permalink
Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeoProc. International Conference on Machine Learning (ICML), 2014.
2013
Similarity Component Analysis Permalink
Soravit Changpinyo*, Kuan Liu*, Fei ShaProc. Conference on Neural Information Processing Systems (NIPS), 2013.
Reshaping Visual Datasets for Domain Adaptation Permalink
Boqing Gong, Kristen Grauman, Fei ShaProc. Conference on Neural Information Processing Systems (NIPS), 2013.
Deformable Spatial Pyramid Matching for Fast Dense Correspondences Permalink
Jaechul Kim, Ce Liu, Fei Sha, Kristen GraumanProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2013.
Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation Permalink
Boqing Gong, Kristen Grauman, Fei ShaProc. International Conference on Machine Learning (ICML), Oral Presentation, 2013.
Analogy-preserving Semantic Embedding for Visual Object Categorization Permalink
Sung Ju Hwang, Kristen Grauman, Fei ShaProc. International Conference on Machine Learning (ICML), 2013.
An alternative text representation to TF-IDF and Bag-of-Words Permalink
Zhixiang (Eddie) Xu, Minmin Chen, Kilian Q Weinberger, Fei ShaarXiv Technical Report, 2013.
2012
Semantic Kernel Forests from Multiple Taxonomies Permalink
Sung Ju Hwang, Kristen Grauman, Fei ShaProc. Conference on Neural Information Processing Systems (NIPS), 2012.
Overcoming Dataset Bias: An Unsupervised Domain Adaptation Approach Permalink
Jaechul Kim, Ce Liu, Fei Sha, Kristen GraumanProc. NIPS Workshop on Large Scale Visual Recognition and Retrieval (BigVision), Oral Presentation, 2012.
Non-linear Metric Learning Permalink
Dor Kedem, Stephen Tyree, Fei Sha, Gert R Lanckriet, Kilian Q WeinbergerProc. Conference on Neural Information Processing Systems (NIPS), 2012.
Cloud-Enabled Privacy-Preserving Collaborative Learning for Mobile Sensing Permalink
Bin Liu, Yurong Jiang, Fei Sha, Ramesh GovindanProc. Conference on Embedded Network Sensor Systems (SenSys), 2012.
From sBoW to dCoT Marginalized Encoders for Text Representation Permalink
Zhixiang (Eddie) Xu, Minmin Chen, Kilian Q Weinberger, Fei ShaProc. International Conference on Information and Knowledge Management, 2012.
Predicting Likability of Speakers with Gaussian Processes Permalink
Minmin Chen, Z Xu, Kilian Q Weinberger, Fei ShaProc. Conference of the International Speech Communication Association (INTERSPEECH), 2012.
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping Permalink
Tomer Levinboim, Fei ShaProc. AAAI Conference on Artificial Intelligence (AAAI), 2012.
Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation Permalink
Yuan Shi, Fei ShaarXiv Technical Report, 2012.
Marginalized Denoising Autoencoders for Domain Adaptation Permalink
Minmin Chen, Zhixiang Xu, Kilian Weinberger, Fei ShaProc. International Conference on Machine Learning (ICML), 2012.
Geodesic Flow Kernel for Unsupervised Domain Adaptation Permalink
Boqing Gong, Yuan Shi, Fei Sha, Kristen GraumanProc. Conference on Computer Vision and Pattern Recognition (CVPR), Oral Presentation, 2012.
2011
Learning a Tree of Metrics with Disjoint Visual Features Permalink
Kristen Grauman, Fei Sha, Sung Ju HwangProc. Conference on Neural Information Processing Systems (NIPS), 2011.
Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features Permalink
Junping Zhang, Ben Tan, Fei Sha, Li HeTransactions on Intelligent Transportation Systems (ITS), 2012.
Learning Discriminative Metrics via Generative Models and Kernel Learning Permalink
Yuan Shi, Yung-Kyun Noh, Fei Sha, Daniel D LeearXiv Technical Report, 2011.
A Study of Web Services Performance Prediction: A Client's Perspective Permalink
Leslie Cheung, Leana Golubchik, Fei ShaProc. International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2011.
Learning with Whom to Share in Multi-task Feature Learning Permalink
Zhuoliang Kang, Kristen Grauman, Fei ShaProc. International Conference on Machine Learning (ICML), 2011.
Sharing features between objects and their attributes Permalink
Sung Ju Hwang, Fei Sha, Kristen GraumanProc. Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
Information Theoretical Clustering via Semidefinite Programming Permalink
Meihong Wang, Fei ShaProc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2011.
Rapid Feature Learning with Stacked Linear Denoisers Permalink
Zhixiang Eddie Xu, Kilian Q Weinberger, Fei ShaarXiv Technical Report, 2011.
Metric learning for reinforcement learning agents Permalink
Matthew E Taylor, Brian Kulis, Fei ShaProc. International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2011.
2010
Unsupervised Kernel Dimension Reduction Permalink
Meihong Wang, Fei Sha, Michael I JordanProc. Conference on Neural Information Processing Systems (NIPS), 2010.
Online Learning and Acoustic Feature Adaptation in Large-Margin Hidden Markov Models Permalink
Chih-Chieh Cheng, Fei Sha, Lawrence K SaulIEEE Journal of Selected Topics in Signal Processing, 2010.
Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner] Permalink
Kilian Q Weinberger, Fei Sha, Lawrence K SaulIEEE Signal Processing Magazine, 2010.
Locally linear denoising on image manifolds Permalink
Dian Gong, Fei Sha, Gérard MedioniProc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
Margin based discriminative training techniques for automatic speech recognition Permalink
Fei Sha, Chih‐Chieh Cheng, Lawrence SaulThe Journal of the Acoustical Society of America, 2010.
Active site prediction using evolutionary and structural information Permalink
Sriram Sankararaman, Fei Sha, Jack F Kirsch, Michael I Jordan, Kimmen SjölanderBioinformatics, 2010.
2009
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification Permalink
Simon Lacoste-Julien, Fei Sha, Michael I JordanProc. Conference on Neural Information Processing Systems (NIPS), 2009.
Large-margin feature adaptation for automatic speech recognition Permalink
Chih-Chieh Cheng, Fei Sha, Lawrence K SaulProc. IEEE Workshop on Automatic Speech Recognition & Understanding, 2009.
A Fast Online Algorithm for Large Margin Training of Continuous Density Hidden Markov Models Permalink
Chih-Chieh Cheng, Fei Sha, Lawrence K SaulProc. Conference of the International Speech Communication Association (INTERSPEECH), 2009.
Robust Web Extraction : An Approach Based on a Probabilistic Tree-Edit Model Permalink
Nilesh Dalvi, Philip Bohannon, Fei ShaProc. ACM SIGMOD International Conference on Management of Data (SIGMOD), 2009.
Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models Permalink
Chih-Chieh Cheng, Fei Sha, Lawrence K SaulProc. International Conference on Machine Learning (ICML), 2009.
2007
Large Margin Hidden Markov Models for Automatic Speech Recognition Permalink
Fei Sha, Lawrence K SaulProc. Conference on Neural Information Processing Systems (NIPS), 2007.
Graph Laplacian Regularization for Large-Scale Semidefinite Programming Permalink
Kilian Q Weinberger, Fei Sha, Qihui Zhu, Lawrence K SaulProc. Conference on Neural Information Processing Systems (NIPS), 2007.
Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification Permalink
Andrea Frome, Yoram Singer, Fei Sha, Jitendra MalikProc. International Conference on Computer Vision (ICCV), 2007.
Multiplicative Updates for L1–Regularized Linear and Logistic Regression Permalink
Fei Sha, Y Albert Park, Lawrence K SaulProc. International Symposium on Intelligent Data Analysis, 2007.
Multiplicative Updates for Nonnegative Quadratic Programming Permalink
Fei Sha, Yuanqing Lin, Lawrence K Saul, Daniel D LeeNeural Computation, 2007.
Regression on Manifolds Using Kernel Dimension Reduction Permalink
Jens Nilsson, Fei Sha, Michael I JordanProc. International Conference on Machine learning (ICML), 2007.
Comparison of Large Margin Training to Other Discriminative Methods for Phonetic Recognition by Hidden Markov Models Permalink
Fei Sha, Lawrence K SaulProc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2007.
Large Margin Training of Acoustic Models for Speech Recognition Permalink
Fei Sha, Lawrence K SaulPhD Dissertation, University of Pennsylvania, 2007.
2006
Large Margin Gaussian Mixture Modeling for Phonetic Classification and Recognition Permalink
Fei Sha, Lawrence K SaulProc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2006.
Spectral Methods for Dimensionality Reduction Permalink
Lawrence K Saul, Kilian Q Weinberger, Jihun H Ham, Fei Sha, Daniel D LeeSemi-Supervised Learning, 2006.
2005
Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization Permalink
Fei Sha, Lawrence K SaulProc. Conference on Neural Information Processing Systems (NIPS), 2005.
Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction Permalink
Fei Sha, Lawrence K SaulProc. International Conference on Machine Learning (ICML), 2005.
2004
Learning a kernel matrix for nonlinear dimensionality reduction Permalink
Kilian Q Weinberger, Fei Sha, Lawrence K SaulProc. International Conference on Machine Learning (ICML), 2004.
Multiband statistical learning for F_0 estimation in speech Permalink
Fei Sha, John Ashley Burgoyne, Lawrence K SaulProc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2004.
2003
Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines Permalink
Fei Sha, Lawrence K Saul, Daniel D LeeProc. Conference on Neural Information Processing Systems (NIPS), 2003.
Statistical Signal Processing with Nonnegativity Constraints Permalink
Lawrence K Saul, Fei Sha, Daniel D LeeProc. European Conference on Speech Communication and Technology, 2003.
Multiplicative Updates for Large Margin Classifiers Permalink
Fei Sha, Lawrence K Saul, Daniel D LeeLearning Theory and Kernel Machines, 2003.
Shallow Parsing with Conditional Random Fields Permalink
Fei Sha, Fernando PereiraProc. Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (NAACL-HLT), 2003.