354 citations
- Institut national de recherche en sciences et technologies du numériqueFR8 papers
- Indian Institute of Technology DelhiIN7 papers
- Microsoft (United States)US7 papers
- Indian Institute of Science BangaloreIN6 papers
- Indian Institute of Technology KanpurIN5 papers
- Indian Institute of Technology KharagpurIN5 papers
- Birla Institute of Technology and Science, Pilani - Goa CampusIN4 papers
- Microsoft Research (United Kingdom)GB4 papers
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6 papers · 1 filter
Semi-Supervised Method using Gaussian Random Fields for Boilerplate Removal in Web Browsers
Joy Bose, Sumanta Mukherjee
Boilerplate removal refers to the problem of removing noisy content from a webpage such as ads and extracting relevant content that can be used by various services. This can be use…
Robust Loss Functions under Label Noise for Deep Neural Networks
Aritra Ghosh, Himanshu Kumar, P. S. Sastry
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particu…
Non-convex Optimization for Machine Learning
Prateek Jain, Purushottam Kar
A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems a…
Reconstruction in the Labeled Stochastic Block Model
Marc Lelarge, Laurent Massoulié, Jiaming Xu
The labeled stochastic block model is a random graph model representing networks with community structure and interactions of multiple types. In its simplest form, it consists of t…
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach
Recently, the Frank-Wolfe optimization algorithm was suggested as a procedure to obtain adaptive quadrature rules for integrals of functions in a reproducing kernel Hilbert space (…
Low-rank Matrix Completion using Alternating Minimization
Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi
Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem…