はてなブックマーク

Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:478-487, 2016. Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method that simul

[edit] Volume 48: International Conference on Machine Learning, 20-22 June 2016, New York, New York, USA [edit] Editors: Maria Florina Balcan, Kilian Q. Weinberger [bib][citeproc] No Oops, You Won’t Do It Again: Mechanisms for Self-correction in Crowdsourcing Nihar Shah, Dengyong Zhou; Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1-10 [abs][Download PDF][Supplement

DRAW: A Recurrent Neural Network For Image Generation Karol Gregor KAROLG@GOOGLE.COM Ivo Danihelka DANIHELKA@GOOGLE.COM Alex Graves GRAVESA@GOOGLE.COM Danilo Jimenez Rezende DANILOR@GOOGLE.COM Daan Wierstra WIERSTRA@GOOGLE.COM Google DeepMind Abstract This paper introduces the Deep Recurrent Atten- tive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel s

Lester Mackey, Ameet Talwalkar, Michael I. Jordan; 16(28):913−960, 2015. Abstract If learning methods are to scale to the massive sizes of modern data sets, it is essential for the field of machine learning to embrace parallel and distributed computing. Inspired by the recent development of matrix factorization methods with rich theory but poor computational complexity and by the relative ease of

[edit] Volume 37: International Conference on Machine Learning, 7-9 July 2015, Lille, France [edit] Editors: Francis Bach, David Blei [bib][citeproc] Stochastic Optimization with Importance Sampling for Regularized Loss Minimization Peilin Zhao, Tong Zhang; Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1-9 [abs][Download PDF] Approval Voting and Incentives in Crowds

Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim; 15(90):3133−3181, 2014. Abstract We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and ot

Steven C.H. Hoi, Jialei Wang, Peilin Zhao; 15(15):495−499, 2014. Abstract LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large- scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made compreh

An Optimal Policy for Target Localization with Application to Electron Microscopy Raphael Sznitman, Aurelien Lucchi, Peter Frazier, Bruno Jedynak, Pascal Fua; Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):1-9 [abs][Download PDF] Domain Generalization via Invariant Feature Representation Krikamol Muandet, David Balduzzi, Bernhard Schölkopf; Proceedings of the 30th

The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon recei