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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