NIPS Proceedingsβ

Ruslan R. Salakhutdinov

35 Papers

  • Deep Sets (2017)
  • Good Semi-supervised Learning That Requires a Bad GAN (2017)
  • Architectural Complexity Measures of Recurrent Neural Networks (2016)
  • Iterative Refinement of the Approximate Posterior for Directed Belief Networks (2016)
  • On Multiplicative Integration with Recurrent Neural Networks (2016)
  • Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations (2016)
  • Review Networks for Caption Generation (2016)
  • Stochastic Variational Deep Kernel Learning (2016)
  • Learning Wake-Sleep Recurrent Attention Models (2015)
  • Path-SGD: Path-Normalized Optimization in Deep Neural Networks (2015)
  • Skip-Thought Vectors (2015)
  • A Multiplicative Model for Learning Distributed Text-Based Attribute Representations (2014)
  • Learning Generative Models with Visual Attention (2014)
  • Annealing between distributions by averaging moments (2013)
  • Discriminative Transfer Learning with Tree-based Priors (2013)
  • Learning Stochastic Feedforward Neural Networks (2013)
  • One-shot learning by inverting a compositional causal process (2013)
  • The Power of Asymmetry in Binary Hashing (2013)
  • A Better Way to Pretrain Deep Boltzmann Machines (2012)
  • Cardinality Restricted Boltzmann Machines (2012)
  • Hamming Distance Metric Learning (2012)
  • Matrix reconstruction with the local max norm (2012)
  • Multimodal Learning with Deep Boltzmann Machines (2012)
  • Learning to Learn with Compound HD Models (2011)
  • Learning with the weighted trace-norm under arbitrary sampling distributions (2011)
  • Transfer Learning by Borrowing Examples for Multiclass Object Detection (2011)
  • Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm (2010)
  • Practical Large-Scale Optimization for Max-norm Regularization (2010)
  • Learning in Markov Random Fields using Tempered Transitions (2009)
  • Modelling Relational Data using Bayesian Clustered Tensor Factorization (2009)
  • Replicated Softmax: an Undirected Topic Model (2009)
  • Evaluating probabilities under high-dimensional latent variable models (2008)
  • Probabilistic Matrix Factorization (2007)
  • Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes (2007)
  • Neighbourhood Components Analysis (2004)