Artificial Intelligence

Learning Granger Causality for Hawkes Processes ICML 2016 Learning Influence Functions from Incomplete Observations NeurIPS 2016 Learning in Games: Robustness of Fast Convergence NeurIPS 2016 Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation JMLR 2016 Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview JMLR 2016 Learning Multiagent Communication with Backpropagation NeurIPS 2016 Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees CVPR 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Planar Ising Models JMLR 2016 Learning Representations for Counterfactual Inference ICML 2016 Learning Sensor Multiplexing Design through Back-propagation NeurIPS 2016 Learning shape correspondence with anisotropic convolutional neural networks NeurIPS 2016 Learning Simple Algorithms from Examples ICML 2016 Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization COLING 2016 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NeurIPS 2016 Learning to Communicate with Deep Multi-Agent Reinforcement Learning NeurIPS 2016 Learning to Filter with Predictive State Inference Machines ICML 2016 Learning to Generate with Memory ICML 2016 Learning to Localize Little Landmarks CVPR 2016 Learning to Poke by Poking: Experiential Learning of Intuitive Physics NeurIPS 2016 Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework CVPR 2016 Learning Tractable Multidimensional Bayesian Network Classifiers PGM 2016 Learning Transferrable Representations for Unsupervised Domain Adaptation NeurIPS 2016 Learning Tree Structured Potential Games NeurIPS 2016 Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables NeurIPS 2016