NIPS Proceedings
^{β}
Books
Russ R. Salakhutdinov
47 Papers
Deep Gamblers: Learning to Abstain with Portfolio Theory
(2019)
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
(2019)
Learning Data Manipulation for Augmentation and Weighting
(2019)
Learning Neural Networks with Adaptive Regularization
(2019)
Mixtape: Breaking the Softmax Bottleneck Efficiently
(2019)
Multiple Futures Prediction
(2019)
On Exact Computation with an Infinitely Wide Neural Net
(2019)
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
(2019)
XLNet: Generalized Autoregressive Pretraining for Language Understanding
(2019)
Deep Generative Models with Learnable Knowledge Constraints
(2018)
GLoMo: Unsupervised Learning of Transferable Relational Graphs
(2018)
How Many Samples are Needed to Estimate a Convolutional Neural Network?
(2018)
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)