Search Results for author: Rafal Jozefowicz

Found 12 papers, 7 papers with code

Learning Dexterous In-Hand Manipulation

no code implementations1 Aug 2018 OpenAI, Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.

Revisiting Distributed Synchronous SGD

no code implementations19 Feb 2017 Xinghao Pan, Jianmin Chen, Rajat Monga, Samy Bengio, Rafal Jozefowicz

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.

Stochastic Optimization

Improved Variational Inference with Inverse Autoregressive Flow

1 code implementation NeurIPS 2016 Durk P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.

Ranked #29 on Image Generation on CIFAR-10 (bits/dimension metric)

Image Generation Variational Inference

LFADS - Latent Factor Analysis via Dynamical Systems

no code implementations22 Aug 2016 David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath

Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously.

Improving Variational Inference with Inverse Autoregressive Flow

8 code implementations15 Jun 2016 Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling

The framework of normalizing flows provides a general strategy for flexible variational inference of posteriors over latent variables.

Variational Inference

Revisiting Distributed Synchronous SGD

4 code implementations4 Apr 2016 Jianmin Chen, Xinghao Pan, Rajat Monga, Samy Bengio, Rafal Jozefowicz

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.

Stochastic Optimization

Exploring the Limits of Language Modeling

10 code implementations7 Feb 2016 Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, Yonghui Wu

In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding.

Language Modelling Language understanding

Generating Sentences from a Continuous Space

11 code implementations CONLL 2016 Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, Samy Bengio

The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation.

Language Modelling

Towards Principled Unsupervised Learning

no code implementations19 Nov 2015 Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, Danilo Rezende, Tim Lillicrap, Oriol Vinyals

Supervised learning is successful because it can be solved by the minimization of the training error cost function.

Domain Adaptation

Fast optimization of Multithreshold Entropy Linear Classifier

no code implementations18 Apr 2015 Rafal Jozefowicz, Wojciech Marian Czarnecki

Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation.

Density Estimation

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