← Core Methods
Machine Learning › Core Methods ›

Representation Learning

18863 directly classified papers

Papers per year

Papers

Relative Density-Ratio Estimation for Robust Distribution Comparison NIPS 2011
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning NIPS 2011
Learning Anchor Planes for Classification NIPS 2011
Learning low-rank output kernels ACML 2011
On Learning Discrete Graphical Models using Greedy Methods NIPS 2011
Variational Gaussian Process Dynamical Systems NIPS 2011
Semi-supervised Regression via Parallel Field Regularization NIPS 2011
Thinning Measurement Models and Questionnaire Design NIPS 2011
Active dendrites: adaptation to spike-based communication NIPS 2011
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning NIPS 2011
Sparse Filtering NIPS 2011
Large-Scale Sparse Principal Component Analysis with Application to Text Data NIPS 2011
Discussion of “Learning Scale Free Networks by Reweighted $\ell_1$ regularization” AISTATS 2011
Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach NIPS 2011
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition NIPS 2011
Variational Learning for Recurrent Spiking Networks NIPS 2011
The Kernel Beta Process NIPS 2011
Learning Higher-Order Graph Structure with Features by Structure Penalty NIPS 2011
An Analysis of Single-Layer Networks in Unsupervised Feature Learning AISTATS 2011
Learning Scale Free Networks by Reweighted $\ell_1$ regularization AISTATS 2011
A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis JMLR 2011
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation NIPS 2011
Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint NIPS 2011
Sequence learning with hidden units in spiking neural networks NIPS 2011
Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment NIPS 2011