Deep Learning

Correcting Sample Selection Bias by Unlabeled Data NeurIPS 2006 Efficient Learning of Sparse Representations with an Energy-Based Model NeurIPS 2006 Efficient sparse coding algorithms NeurIPS 2006 Efficient Structure Learning of Markov Networks using L_1-Regularization NeurIPS 2006 Emergence of conjunctive visual features by quadratic independent component analysis NeurIPS 2006 Fast Computation of Graph Kernels NeurIPS 2006 Fast Discriminative Visual Codebooks using Randomized Clustering Forests NeurIPS 2006 Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space NeurIPS 2006 Greedy Layer-Wise Training of Deep Networks NeurIPS 2006 Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons NeurIPS 2006 Large Margin Hidden Markov Models for Automatic Speech Recognition NeurIPS 2006 Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis NeurIPS 2006 Large Scale Hidden Semi-Markov SVMs NeurIPS 2006 Large-Scale Sparsified Manifold Regularization NeurIPS 2006 Learning Dense 3D Correspondence NeurIPS 2006 Learning Image Components for Object Recognition JMLR 2006 Learning Nonparametric Models for Probabilistic Imitation NeurIPS 2006 Learning on Graph with Laplacian Regularization NeurIPS 2006 Learning Parts-Based Representations of Data JMLR 2006 Learning with Hypergraphs: Clustering, Classification, and Embedding NeurIPS 2006 Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples JMLR 2006 Map-Reduce for Machine Learning on Multicore NeurIPS 2006 Modeling Dyadic Data with Binary Latent Factors NeurIPS 2006 Modeling Human Motion Using Binary Latent Variables NeurIPS 2006 Multi-Instance Multi-Label Learning with Application to Scene Classification NeurIPS 2006