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Representation Learning

18863 directly classified papers

Papers per year

Papers

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016
Representational Similarity Learning with Application to Brain Networks ICML 2016
Structured Prediction Energy Networks ICML 2016
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow ICML 2016
Boolean Matrix Factorization and Noisy Completion via Message Passing ICML 2016
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation ICML 2016
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms ICML 2016
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams ICML 2016
Variable Elimination in the Fourier Domain ICML 2016
Why Regularized Auto-Encoders learn Sparse Representation? ICML 2016
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA ICML 2016
Diversity-Promoting Bayesian Learning of Latent Variable Models ICML 2016
Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks INTERSPEECH 2016
Multi-View Deep Network for Cross-View Classification CVPR 2016
Nonparametric Canonical Correlation Analysis ICML 2016
Neural Paraphrase Generation with Stacked Residual LSTM Networks COLING 2016
Latent Topic Embedding COLING 2016
Fast Zero-Shot Image Tagging CVPR 2016
Supervised Learning with Tensor Networks NIPS 2016
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis NIPS 2016
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics NIPS 2016
Disentangling factors of variation in deep representation using adversarial training NIPS 2016
Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation CVPR 2016
Learning Kernels with Random Features NIPS 2016
A Pseudo-Bayesian Algorithm for Robust PCA NIPS 2016