representation learning

Data dependent kernels in nearly-linear time AISTATS 2012 Deep Boltzmann Machines as Feed-Forward Hierarchies AISTATS 2012 Deep Representations and Codes for Image Auto-Annotation NeurIPS 2012 Exploiting Unrelated Tasks in Multi-Task Learning AISTATS 2012 Hamming Distance Metric Learning NeurIPS 2012 Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing AISTATS 2012 Learning the Dependency Structure of Latent Factors NeurIPS 2012 Learning with Recursive Perceptual Representations NeurIPS 2012 Multimodal Learning with Deep Boltzmann Machines NeurIPS 2012 On Nonparametric Guidance for Learning Autoencoder Representations AISTATS 2012 Transferring Expectations in Model-based Reinforcement Learning NeurIPS 2012 An Analysis of Single-Layer Networks in Unsupervised Feature Learning AISTATS 2011 Deep Learners Benefit More from Out-of-Distribution Examples AISTATS 2011 Discussion of “The Neural Autoregressive Distribution Estimator” AISTATS 2011 Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection NeurIPS 2011 Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning AISTATS 2011 Extracting Speaker-Specific Information with a Regularized Siamese Deep Network NeurIPS 2011 Kernel Analysis of Deep Networks JMLR 2011 PiCoDes: Learning a Compact Code for Novel-Category Recognition NeurIPS 2011 Selecting Receptive Fields in Deep Networks NeurIPS 2011 Shallow vs. Deep Sum-Product Networks NeurIPS 2011 Similarity-based Learning via Data Driven Embeddings NeurIPS 2011 Sparse Filtering NeurIPS 2011 The Manifold Tangent Classifier NeurIPS 2011 Two-Layer Multiple Kernel Learning AISTATS 2011