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Fei Sha - Publications

Affiliations: 
2002-2006 Computer Science University of Pennsylvania, Philadelphia, PA, United States 
Area:
Statistical Machine Learning, Principled Probabilistic Models and Algorithms
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38 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2022 Hu H, Sener O, Sha F, Koltun V. Drinking from a Firehose: Continual Learning with Web-scale Natural Language. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 36315549 DOI: 10.1109/TPAMI.2022.3218265  0.741
2020 Rawat RR, Ortega I, Roy P, Sha F, Shibata D, Ruderman D, Agus DB. Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images. Scientific Reports. 10: 7275. PMID 32350370 DOI: 10.1038/S41598-020-64156-4  0.402
2019 May A, Garakani AB, Lu Z, Guo D, Liu K, Bellet A, Fan L, Collins M, Hsu DJ, Kingsbury B, Picheny M, Sha F. Kernel Approximation Methods for Speech Recognition Journal of Machine Learning Research. 20: 1-36. DOI: 10.7916/D8D80P9T  0.461
2019 Changpinyo S, Chao W, Gong B, Sha F. Classifier and Exemplar Synthesis for Zero-Shot Learning International Journal of Computer Vision. 128: 166-201. DOI: 10.1007/S11263-019-01193-1  0.794
2016 Potthast C, Breitenmoser A, Sha F, Sukhatme GS. Active multi-view object recognition: A unifying view on online feature selection and view planning Robotics and Autonomous Systems. 84: 31-47. DOI: 10.1016/J.Robot.2016.06.013  0.355
2015 Liu K, Bellet A, Sha F. Similarity learning for high-dimensional sparse data Journal of Machine Learning Research. 38: 653-662.  0.472
2015 Chen M, Weinberger KQ, Xu Z, Sha F. Marginalizing Stacked linear denoising autoencoders Journal of Machine Learning Research. 16: 3849-3875.  0.728
2014 Gong B, Grauman K, Sha F. Learning kernels for unsupervised domain adaptation with applications to visual object recognition International Journal of Computer Vision. 109: 3-27. DOI: 10.1007/S11263-014-0718-4  0.679
2014 Chen M, Weinberger K, Sha F, Bengio Y. Marginalized denoising auto-encoders for nonlinear representations 31st International Conference On Machine Learning, Icml 2014. 4: 3342-3350.  0.64
2014 Wang J, Sun K, Sha F, Marchand-Maillet S, Kalousis A. Two-stage metric learning 31st International Conference On Machine Learning, Icml 2014. 2: 1683-1692.  0.455
2014 Shi Y, Bellet A, Sha F. Sparse compositional metric learning Proceedings of the National Conference On Artificial Intelligence. 3: 2078-2084.  0.502
2013 Gong B, Grauman K, Sha F. Connecting the dots with landmarks: Discriminatively learning domain-invariant features for unsupervised domain adaptation 30th International Conference On Machine Learning, Icml 2013. 222-230.  0.314
2013 Changpinyo S, Liu K, Sha F. Similarity component analysis Advances in Neural Information Processing Systems.  0.301
2012 Liu B, Jiang Y, Sha F, Govindan R. Cloud-enabled privacy-preserving collaborative learning for mobile sensing Sensys 2012 - Proceedings of the 10th Acm Conference On Embedded Networked Sensor Systems. 57-70. DOI: 10.1145/2426656.2426663  0.348
2012 Xu Z, Chen M, Weinberger KQ, Sha F. From sBoW to dCoT marginalized encoders for text representation Acm International Conference Proceeding Series. 1879-1884. DOI: 10.1145/2396761.2398536  0.715
2012 Shi Y, Sha F. Information-theoretical learning of discriminative clusters for unsupervised domain adaptation Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1079-1086.  0.384
2012 Lu D, Sha F. Predicting likability of speakers with Gaussian processes 13th Annual Conference of the International Speech Communication Association 2012, Interspeech 2012. 1: 286-289.  0.393
2012 Kedem D, Tyree S, Weinberger KQ, Sha F, Lanckriet G. Non-linear metric learning Advances in Neural Information Processing Systems. 4: 2573-2581.  0.693
2012 Levinboim T, Sha F. Learning the kernel matrix with low-rank multiplicative shaping Proceedings of the National Conference On Artificial Intelligence. 2: 984-990.  0.452
2012 Chen M, Xu Z, Weinberger KQ, Sha F. Marginalized denoising autoencoders for domain adaptation Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 767-774.  0.722
2011 Zhang J, Tan B, Sha F, He L. Predicting pedestrian counts in crowded scenes with rich and high-dimensional features Ieee Transactions On Intelligent Transportation Systems. 12: 1037-1046. DOI: 10.1109/Tits.2011.2132759  0.491
2011 Kang Z, Grauman K, Sha F. Learning with whom to share in multi-task feature learning Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 521-528.  0.417
2011 Hwang SJ, Grauman K, Sha F. Learning a tree of metrics with disjoint visual features Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011.  0.36
2011 Taylor ME, Kulis B, Sha F. Metric learning for reinforcement learning agents 10th International Conference On Autonomous Agents and Multiagent Systems 2011, Aamas 2011. 2: 729-736.  0.431
2010 Sankararaman S, Sha F, Kirsch JF, Jordan MI, Sjölander K. Active site prediction using evolutionary and structural information. Bioinformatics (Oxford, England). 26: 617-24. PMID 20080507 DOI: 10.1093/Bioinformatics/Btq008  0.664
2010 Sha F, Cheng C, Saul L. Margin based discriminative training techniques for automatic speech recognition. The Journal of the Acoustical Society of America. 127: 2041-2041. DOI: 10.1121/1.3385375  0.776
2010 Weinberger K, Sha F, Saul L. Convex optimizations for distance metric learning and pattern classification Ieee Signal Processing Magazine. 27: 146-150+158. DOI: 10.1109/Msp.2010.936013  0.785
2010 Cheng CC, Sha F, Saul LK. Online learning and acoustic feature adaptation in large-Margin hidden Markov models Ieee Journal On Selected Topics in Signal Processing. 4: 926-942. DOI: 10.1109/Jstsp.2010.2048607  0.787
2010 Wang M, Sha F, Jordan MI. Unsupervised kernel dimension reduction Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010.  0.588
2009 Cheng CC, Sha F, Saul LK. Matrix updates for perceptron training of continuous density hidden Markov models Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 153-160. DOI: 10.1145/1553374.1553394  0.744
2009 Cheng CC, Sha F, Saul LK. Large-margin feature adaptation for automatic speech recognition Proceedings of the 2009 Ieee Workshop On Automatic Speech Recognition and Understanding, Asru 2009. 87-92. DOI: 10.1109/ASRU.2009.5373320  0.762
2009 Cheng CC, Sha F, Saul LK. A fast online algorithm for large margin training of continuous density hidden Markov models Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech. 668-671.  0.758
2009 Lacoste-Julien S, Sha F, Jordan MI. DiscLDA: Discriminative learning for dimensionality reduction and classification Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 897-904.  0.789
2007 Sha F, Lin Y, Saul LK, Lee DD. Multiplicative updates for nonnegative quadratic programming. Neural Computation. 19: 2004-31. PMID 17571937 DOI: 10.1162/Neco.2007.19.8.2004  0.755
2007 Nilsson J, Sha F, Jordan MI. Regression on manifolds using kernel dimension reduction Acm International Conference Proceeding Series. 227: 697-704. DOI: 10.1145/1273496.1273584  0.579
2007 Frome A, Sha F, Singer Y, Malik J. Learning globally-consistent local distance functions for shape-based image retrieval and classification Proceedings of the Ieee International Conference On Computer Vision. DOI: 10.1109/ICCV.2007.4408839  0.337
2007 Sha F, Saul LK. Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 4. DOI: 10.1109/ICASSP.2007.366912  0.743
2003 Sha F, Saul LK, Lee DD. Multiplicative updates for large margin classifiers Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2777: 188-202.  0.729
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