Jason J. Corso

Prof. Corso moved to the Electrical Engineering and Computer Science department at the University of Michigan in the 8/2014. He continues his work and research group in high-level computer vision at the intersection of perception, semantics/language, and robotics. Unless you are looking for something specific, historically, here, you probably would rather go to his new page.

tag: machine learning

[1]

C. Xiong, S. McCloskey, and J. J. Corso. Latent domains for visual domain adaptation. In Proceedings of AAAI Conference on Artificial Intelligence, 2014. [ bib ]

[2]

C. Xiong, D. M. Johnson, and J. J. Corso. Active clustering with model-based uncertainty reduction. Technical Report 1402.1783, arXiv, 2014. [ bib | .pdf ]

[3]

C. Xiong, W. Chen, G. Chen, D. Johnson, and J. J. Corso. Adaptive quantization: An information-based approach to learning binary codes. In Proceedings of SIAM International Conference on Data Mining, 2014. [ bib | code | .pdf ]

[4]

C. Xiong, D. M. Johnson, and J. J. Corso. Uncertainty reduction for active image clustering via a hybrid global-local uncertainty model. In Proceedings of AAAI Conference on Artificial Intelligence (Late-Breaking Papers Track), 2013. [ bib | .pdf ]

[5]

D. M. Johnson, C. Xiong, J. Gao, and J. J. Corso. Comprehensive cross-hierarchy cluster agreement evaluation. In Proceedings of AAAI Conference on Artificial Intelligence (Late-Breaking Papers Track), 2013. [ bib | code | .pdf ]

[6]

N. Coffee, J. Gawley, C. W. Forstall, W. J. Scheirer, D. Johnson, J. J. Corso, and B. Parks. Modelling the interpretation of literary allusion with machine learning techniques. In Proceedings of Digital Humanities, 2013. [ bib ]

[7]

P. Das, R. K. Srihari, and J. J. Corso. Translating related words to videos and back through latent topics. In Proceedings of Sixth ACM International Conference on Web Search and Data Mining, 2013. [ bib | .pdf ]

[8]

K. R. Keane and J. J. Corso. Maintaining prior distributions across evolving eigenspaces: An application to portfolio construction. In Proceedings of 11th International Conference on Machine Learning and Applications, 2012. [ bib | .pdf ]

[9]

C. Xiong and J. J. Corso. Coaction discovery: Segmentation of common actions across multiple videos. In Proceedings of Multimedia Data Mining Workshop in Conjunction with the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (MDMKDD), 2012. [ bib | .pdf ]

[10]

C. Xiong, D. Johnson, and J. J. Corso. Efficient max-margin metric learning. In Proceedings of European Conference on Data Mining, 2012. Winner of Best Paper Award at ECDM 2012. [ bib | .pdf ]

[11]

C. Xiong, D. Johnson, and J. J. Corso. Spectral active clustering via purification of the k-nearest neighbor graph. In Proceedings of European Conference on Data Mining, 2012. [ bib | .pdf ]

[12]

C. Xiong, D. Johnson, R. Xu, and J. J. Corso. Random forests for metric learning with implicit pairwise position dependence. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012. [ bib | slides | code | .pdf ]

[13]

K. R. Keane and J. J. Corso. Dynamically mixing dynamic linear models with applications in finance. In Proceedings of International Conference on Pattern Recognition Applications and Methods, 2012. [ bib | .pdf ]

[14]

H. Z. Girgis, J. J. Corso, and D. Fischer. On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures. In Proceedings of IEEE Conference on Engineering in Medicine and Biology, volume 1, pages 4949-4953, 2009. [ bib | .pdf ]

[15]

H. Girgis and J. J. Corso. STP: The Sample-Train-Predict Algorithm and Its Application to Protein Structure Meta-Selection. Technical Report 2008-16, University at Buffalo SUNY, 2008. [ bib ]