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Self-Supervised Learning

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Papers

Event-Specific Image Importance CVPR 2016 Generative Adversarial Imitation Learning NeurIPS 2016 Hierarchically Gated Deep Networks for Semantic Segmentation CVPR 2016 Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks JMLR 2016 Identifying Good Training Data for Self-Supervised Free Space Estimation CVPR 2016 iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning CVPR 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition NeurIPS 2016 Learning Aligned Cross-Modal Representations From Weakly Aligned Data CVPR 2016 Learning to Localize Little Landmarks CVPR 2016 Multi-task Prediction of Disease Onsets from Longitudinal Laboratory Tests MLHC 2016 Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks NeurIPS 2016 Processing and Adaptation to Ambiguous Sounds during the Course of Perceptual Learning Interspeech 2016 Real-Time Tracking of Speakers’ Emotions, States, and Traits on Mobile Platforms Interspeech 2016 Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis COLING 2016 Regularizing Long Short Term Memory With 3D Human-Skeleton Sequences for Action Recognition CVPR 2016 Relevant sparse codes with variational information bottleneck NeurIPS 2016 Slow and Steady Feature Analysis: Higher Order Temporal Coherence in Video CVPR 2016 Speech Emotion Recognition Using Affective Saliency Interspeech 2016 TASTY: Interactive Entity Linking As-You-Type COLING 2016 Temporal Multimodal Learning in Audiovisual Speech Recognition CVPR 2016 The Information Sieve ICML 2016 Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks Interspeech 2016 Tweet Sarcasm Detection Using Deep Neural Network COLING 2016 Using Self-Contradiction to Learn Confidence Measures in Stereo Vision CVPR 2016