Lista de obras - Ruslan Salakhutdinov - Dominio Público Uruguay

A Better Way to Pretrain Deep Boltzmann Machines ⬇️

artículo científico publicado en 2012

A Closer Look at Accuracy vs. Robustness ⬇️

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations ⬇️

artículo científico publicado en 2014

An efficient learning procedure for deep Boltzmann machines

artículo científico publicado en 2012

Annealing between distributions by averaging moments ⬇️

Architectural Complexity Measures of Recurrent Neural Networks ⬇️

artículo científico publicado en 2016

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models ⬇️

artículo científico publicado en 2022

Cardinality Restricted Boltzmann Machines ⬇️

artículo científico publicado en 2012

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm ⬇️

artículo científico publicado en 2010

Deep Gamblers: Learning to Abstain with Portfolio Theory ⬇️

Deep Generative Models with Learnable Knowledge Constraints ⬇️

Deep Sets ⬇️

Deep learning for neuroimaging: a validation study

artículo científico publicado en 2014

Discovering binary codes for documents by learning deep generative models

artículo científico

Discriminative Transfer Learning with Tree-based Priors ⬇️

Dropout: A Simple Way to Prevent Neural Networks from Overfitting ⬇️

article by Nitish Srivastava et al published 2014 in Journal of Machine Learning Research

Evaluating probabilities under high-dimensional latent variable models ⬇️

Evaluation methods for topic models

GLoMo: Unsupervised Learning of Transferable Relational Graphs ⬇️

article

Generating Images from Captions with Attention

Good Semi-supervised Learning That Requires a Bad GAN ⬇️

artículo científico publicado en 2017

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels ⬇️

Hamming Distance Metric Learning ⬇️

artículo científico publicado en 2012

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering ⬇️

How Many Samples are Needed to Estimate a Convolutional Neural Network? ⬇️

article

Human-level concept learning through probabilistic program induction

artículo científico publicado en 2015

Iterative Refinement of the Approximate Posterior for Directed Belief Networks ⬇️

artículo científico publicado en 2016

Learning Data Manipulation for Augmentation and Weighting ⬇️

artículo científico publicado en 2019

Learning Deep Generative Models

artículo científico publicado en 2015

Learning Generative Models with Visual Attention ⬇️

artículo científico publicado en 2014

Learning Neural Networks with Adaptive Regularization ⬇️

Learning Stochastic Feedforward Neural Networks ⬇️

scholarly article by Charlie Tang & Ruslan Salakhutdinov published 2013 in Advances in Neural Information Processing Systems 26

Learning Wake-Sleep Recurrent Attention Models ⬇️

Learning in Markov Random Fields using Tempered Transitions ⬇️

artículo científico publicado en 2009

Learning to Learn with Compound HD Models ⬇️

artículo científico publicado en 2011

Learning with hierarchical-deep models

artículo científico publicado en 2013

Learning with the weighted trace-norm under arbitrary sampling distributions ⬇️

artículo científico publicado en 2011

Matrix reconstruction with the local max norm ⬇️

artículo científico publicado en 2012

Modelling Relational Data using Bayesian Clustered Tensor Factorization ⬇️

artículo científico publicado en 2009

Multimodal Learning with Deep Boltzmann Machines ⬇️

artículo científico publicado en 2012

Multimodal Transformer for Unaligned Multimodal Language Sequences

scientific article published on 01 July 2019

Multiple Futures Prediction ⬇️

artículo científico publicado en 2019

Neighbourhood Components Analysis ⬇️

artículo científico publicado en 2005

Neural Methods for Point-wise Dependency Estimation ⬇️

artículo científico publicado en 2020

Object Goal Navigation using Goal-Oriented Semantic Exploration ⬇️

artículo científico publicado en 2020

On Exact Computation with an Infinitely Wide Neural Net ⬇️

On Multiplicative Integration with Recurrent Neural Networks ⬇️

artículo científico publicado en 2016

On Reward-Free Reinforcement Learning with Linear Function Approximation ⬇️

scholarly article by Ruosong Wang et al published November 2020 in Advances in Neural Information Processing Systems 33

On the Complexity of Exploration in Goal-Driven Navigation ⬇️

One-shot learning by inverting a compositional causal process ⬇️

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text ⬇️

artículo científico publicado en 2018

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations ⬇️

artículo científico publicado en 2016

Path-SGD: Path-Normalized Optimization in Deep Neural Networks ⬇️

Planning with General Objective Functions: Going Beyond Total Rewards ⬇️

artículo científico publicado en 2020

Practical Large-Scale Optimization for Max-norm Regularization ⬇️

Probabilistic Matrix Factorization ⬇️

artículo científico publicado en 2008

Reducing the dimensionality of data with neural networks

artículo científico publicado en 2006

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension ⬇️

artículo científico publicado en 2020

Replicated Softmax: an Undirected Topic Model

scholarly article

Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks

artículo científico publicado en 2014

Review Networks for Caption Generation ⬇️

artículo científico publicado en 2016

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement ⬇️

artículo científico publicado en 2020

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning ⬇️

artículo científico publicado en 2019

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

scholarly article

Skip-Thought Vectors ⬇️

artículo científico publicado en 2015

Stochastic Variational Deep Kernel Learning ⬇️

artículo científico publicado en 2016

The More You Know: Using Knowledge Graphs for Image Classification

artículo científico publicado en 2017

The Power of Asymmetry in Binary Hashing ⬇️

Transfer Learning by Borrowing Examples for Multiclass Object Detection ⬇️

artículo científico publicado en 2011

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context ⬇️

artículo científico publicado en 2019

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models ⬇️

artículo científico publicado en 2014

Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes ⬇️

artículo científico publicado en 2008

Weakly-Supervised Reinforcement Learning for Controllable Behavior ⬇️

artículo científico publicado en 2020

XLNet: Generalized Autoregressive Pretraining for Language Understanding ⬇️

artículo científico publicado en 2019

Lista de obras de Ruslan Salakhutdinov

A Better Way to Pretrain Deep Boltzmann Machines ⬇️

A Closer Look at Accuracy vs. Robustness ⬇️

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations ⬇️

An efficient learning procedure for deep Boltzmann machines

Annealing between distributions by averaging moments ⬇️

Architectural Complexity Measures of Recurrent Neural Networks ⬇️

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models ⬇️

Cardinality Restricted Boltzmann Machines ⬇️

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm ⬇️

Deep Gamblers: Learning to Abstain with Portfolio Theory ⬇️

Deep Generative Models with Learnable Knowledge Constraints ⬇️

Deep Sets ⬇️

Deep learning for neuroimaging: a validation study

Discovering binary codes for documents by learning deep generative models

Discriminative Transfer Learning with Tree-based Priors ⬇️

Dropout: A Simple Way to Prevent Neural Networks from Overfitting ⬇️

Evaluating probabilities under high-dimensional latent variable models ⬇️

Evaluation methods for topic models

GLoMo: Unsupervised Learning of Transferable Relational Graphs ⬇️

Generating Images from Captions with Attention

Good Semi-supervised Learning That Requires a Bad GAN ⬇️

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels ⬇️

Hamming Distance Metric Learning ⬇️

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering ⬇️

How Many Samples are Needed to Estimate a Convolutional Neural Network? ⬇️

Human-level concept learning through probabilistic program induction

Iterative Refinement of the Approximate Posterior for Directed Belief Networks ⬇️

Learning Data Manipulation for Augmentation and Weighting ⬇️

Learning Deep Generative Models

Learning Generative Models with Visual Attention ⬇️

Learning Neural Networks with Adaptive Regularization ⬇️

Learning Stochastic Feedforward Neural Networks ⬇️

Learning Wake-Sleep Recurrent Attention Models ⬇️

Learning in Markov Random Fields using Tempered Transitions ⬇️

Learning to Learn with Compound HD Models ⬇️

Learning with hierarchical-deep models

Learning with the weighted trace-norm under arbitrary sampling distributions ⬇️

Matrix reconstruction with the local max norm ⬇️

Modelling Relational Data using Bayesian Clustered Tensor Factorization ⬇️

Multimodal Learning with Deep Boltzmann Machines ⬇️

Multimodal Transformer for Unaligned Multimodal Language Sequences

Multiple Futures Prediction ⬇️

Neighbourhood Components Analysis ⬇️

Neural Methods for Point-wise Dependency Estimation ⬇️

Object Goal Navigation using Goal-Oriented Semantic Exploration ⬇️

On Exact Computation with an Infinitely Wide Neural Net ⬇️

On Multiplicative Integration with Recurrent Neural Networks ⬇️

On Reward-Free Reinforcement Learning with Linear Function Approximation ⬇️

On the Complexity of Exploration in Goal-Driven Navigation ⬇️

One-shot learning by inverting a compositional causal process ⬇️

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text ⬇️

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations ⬇️

Path-SGD: Path-Normalized Optimization in Deep Neural Networks ⬇️

Planning with General Objective Functions: Going Beyond Total Rewards ⬇️

Practical Large-Scale Optimization for Max-norm Regularization ⬇️

Probabilistic Matrix Factorization ⬇️

Reducing the dimensionality of data with neural networks

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension ⬇️

Replicated Softmax: an Undirected Topic Model

Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks

Review Networks for Caption Generation ⬇️

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement ⬇️

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning ⬇️

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Skip-Thought Vectors ⬇️

Stochastic Variational Deep Kernel Learning ⬇️

The More You Know: Using Knowledge Graphs for Image Classification

The Power of Asymmetry in Binary Hashing ⬇️

Transfer Learning by Borrowing Examples for Multiclass Object Detection ⬇️

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context ⬇️

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models ⬇️

Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes ⬇️

Weakly-Supervised Reinforcement Learning for Controllable Behavior ⬇️

XLNet: Generalized Autoregressive Pretraining for Language Understanding ⬇️