We consider the problem of learning deep representation when target labels are available. In this paper, we show that there exists intrinsic relationship between target coding and...
Shuo Yang, Ping Luo, Chen Change Loy, Kenneth W. S...
In many real-world situations a decision maker may make decisions across many separate reinforcement learning tasks in parallel, yet there has been very little work on concurrent ...
Modern organizations (e.g., hospitals, social networks, government agencies) rely heavily on audit to detect and punish insiders who inappropriately access and disclose confident...
Jeremiah Blocki, Nicolas Christin, Anupam Datta, A...
Pronoun resolution and common noun phrase resolution are the two most challenging subtasks of coreference resolution. While a lot of work has focused on pronoun resolution, common...
Motivated by problems such as molecular energy prediction, we derive an (improper) kernel between geometric inputs, that is able to capture the relevant rotational and translation...
We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms. Recently this approach was explored in Bento e...
Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a subcommunity of machine learning has focused on solving thi...
Matthias Feurer, Jost Tobias Springenberg, Frank H...
We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approa...
Sherry Shanshan Ruan, Gheorghe Comanici, Prakash P...
Matching and merging data from conflicting sources is the bread and butter of data integration, which drives search verticals, e-commerce comparison sites and cyber intelligence....