Deep Learning

Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling NeurIPS 2016 Learning Attributes Equals Multi-Source Domain Generalization CVPR 2016 Learning Compact Binary Descriptors With Unsupervised Deep Neural Networks CVPR 2016 Learning Convolutional Neural Networks for Graphs ICML 2016 Learning Deep Embeddings with Histogram Loss NeurIPS 2016 Learning Deep Feature Representations With Domain Guided Dropout for Person Re-Identification CVPR 2016 Learning Deep Features for Discriminative Localization CVPR 2016 Learning Deep Parsimonious Representations NeurIPS 2016 Learning Deep Representation for Imbalanced Classification CVPR 2016 Learning Deep Structure-Preserving Image-Text Embeddings CVPR 2016 Learning Dense Correspondence via 3D-Guided Cycle Consistency CVPR 2016 Learning End-to-end Video Classification with Rank-Pooling ICML 2016 Learning feed-forward one-shot learners NeurIPS 2016 Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs NeurIPS 2016 Learning From the Mistakes of Others: Matching Errors in Cross-Dataset Learning CVPR 2016 Learning Granger Causality for Hawkes Processes ICML 2016 Learning Local Image Descriptors With Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions CVPR 2016 Learning Multiagent Communication with Backpropagation NeurIPS 2016 Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 Learning Multiscale Features Directly from Waveforms Interspeech 2016 Learning Neural Network Representations Using Cross-Lingual Bottleneck Features with Word-Pair Information Interspeech 2016 Learning Physical Intuition of Block Towers by Example ICML 2016 Learning Population-Level Diffusions with Generative RNNs ICML 2016 Learning Relaxed Deep Supervision for Better Edge Detection CVPR 2016 Learning Robust Features using Deep Learning for Automatic Seizure Detection MLHC 2016