Search Results for author: Wojciech Zaremba

Found 31 papers, 20 papers with code

A Generalizable Approach to Learning Optimizers

1 code implementation2 Jun 2021 Diogo Almeida, Clemens Winter, Jie Tang, Wojciech Zaremba

A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.

Language Modelling

Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

no code implementations27 Sep 2020 Lei M. Zhang, Matthias Plappert, Wojciech Zaremba

We further show that the transfer metric can predict the effect of training setups on policy transfer performance.

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.


Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

no code implementations18 Oct 2017 Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel

By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.

Robotics Systems and Control

Asymmetric Actor Critic for Image-Based Robot Learning

no code implementations18 Oct 2017 Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator.

Decision Making

Domain Randomization and Generative Models for Robotic Grasping

no code implementations17 Oct 2017 Joshua Tobin, Lukas Biewald, Rocky Duan, Marcin Andrychowicz, Ankur Handa, Vikash Kumar, Bob McGrew, Jonas Schneider, Peter Welinder, Wojciech Zaremba, Pieter Abbeel

In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.

Robotic Grasping

One-Shot Imitation Learning

no code implementations NeurIPS 2017 Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, Wojciech Zaremba

A neural net is trained that takes as input one demonstration and the current state (which initially is the initial state of the other demonstration of the pair), and outputs an action with the goal that the resulting sequence of states and actions matches as closely as possible with the second demonstration.

Feature Engineering Imitation Learning +1

Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

4 code implementations20 Mar 2017 Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel

Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.

Object Localization

Extensions and Limitations of the Neural GPU

1 code implementation2 Nov 2016 Eric Price, Wojciech Zaremba, Ilya Sutskever

We find that these techniques increase the set of algorithmic problems that can be solved by the Neural GPU: we have been able to learn to perform all the arithmetic operations (and generalize to arbitrarily long numbers) when the arguments are given in the decimal representation (which, surprisingly, has not been possible before).

Learning Simple Algorithms from Examples

1 code implementation23 Nov 2015 Wojciech Zaremba, Tomas Mikolov, Armand Joulin, Rob Fergus

We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples.


Convolutional networks and learning invariant to homogeneous multiplicative scalings

no code implementations26 Jun 2015 Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba

The conventional classification schemes -- notably multinomial logistic regression -- used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation.

Classification General Classification

Reinforcement Learning Neural Turing Machines - Revised

1 code implementation4 May 2015 Wojciech Zaremba, Ilya Sutskever

The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world.


Addressing the Rare Word Problem in Neural Machine Translation

5 code implementations IJCNLP 2015 Minh-Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, Wojciech Zaremba

Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2. 8 BLEU points over an equivalent NMT system that does not use this technique.

Machine Translation Translation +1

Learning to Execute

6 code implementations17 Oct 2014 Wojciech Zaremba, Ilya Sutskever

Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.

Learning to Execute

Recurrent Neural Network Regularization

20 code implementations8 Sep 2014 Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.

Image Captioning Machine Translation +2

Learning to Discover Efficient Mathematical Identities

1 code implementation NeurIPS 2014 Wojciech Zaremba, Karol Kurach, Rob Fergus

In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities.

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

no code implementations NeurIPS 2014 Emily Denton, Wojciech Zaremba, Joan Bruna, Yann Lecun, Rob Fergus

We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks.

Object Recognition

Spectral Networks and Locally Connected Networks on Graphs

4 code implementations21 Dec 2013 Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann Lecun

Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes over their domain.


Intriguing properties of neural networks

11 code implementations21 Dec 2013 Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks.

B-test: A Non-parametric, Low Variance Kernel Two-sample Test

no code implementations NeurIPS 2013 Wojciech Zaremba, Arthur Gretton, Matthew Blaschko

We propose a family of maximum mean discrepancy (MMD) kernel two-sample tests that have low sample complexity and are consistent.

B-tests: Low Variance Kernel Two-Sample Tests

1 code implementation8 Jul 2013 Wojciech Zaremba, Arthur Gretton, Matthew Blaschko

A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced.

Two-sample testing

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