Papers
Learning Representations for Counterfactual Inference
Fredrik Johansson, Uri Shalit, David Sontag
Learning Robust Features using Deep Learning for Automatic Seizure Detection
Pierre Thodoroff, Joelle Pineau, Andrew Lim
Learning Sensor Multiplexing Design through Back-propagation
Ayan Chakrabarti
Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà et al.
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
Zhao Song, Ricardo Henao, David Carlson et al.
Learning Simple Algorithms from Examples
Wojciech Zaremba, Tomas Mikolov, Armand Joulin et al.
Learning Simple Auctions
Jamie Morgenstern, Tim Roughgarden
Learning Sparse Additive Models with Interactions in High Dimensions
Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner et al.
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause et al.
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Mohammad Javad Hosseini, Su-In Lee
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Varun Jampani, Martin Kiefel, Peter V. Gehler
Learning Structured Inference Neural Networks With Label Relations
Hexiang Hu, Guang-Tong Zhou, Zhiwei Deng et al.
Learning Structured Low-Rank Representation via Matrix Factorization
Jie Shen, Ping Li
Learning Structured Sparsity in Deep Neural Networks
Wei Wen, Chunpeng Wu, Yandan Wang et al.
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
Lev Bogolubsky, Pavel Dvurechenskii, Alexander Gasnikov et al.
Learning Taxonomy Adaptation in Large-scale Classification
Rohit Babbar, Ioannis Partalas, Eric Gaussier et al.
Learning Temporal Regularity in Video Sequences
Mahmudul Hasan, Jonghyun Choi, Jan Neumann et al.
Learning the Number of Neurons in Deep Networks
Jose M Alvarez, Mathieu Salzmann
Learning Theory for Distribution Regression
Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos et al.
Learning the Variance of the Reward-To-Go
Aviv Tamar, Dotan Di Castro, Shie Mannor
Learning to Assign Orientations to Feature Points
Kwang Moo Yi, Yannick Verdie, Pascal Fua et al.
Learning to Co-Generate Object Proposals With a Deep Structured Network
Zeeshan Hayder, Xuming He, Mathieu Salzmann
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas et al.
Learning to Distill: The Essence Vector Modeling Framework
Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen et al.