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Found 31 papers, 20 papers with code
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
A Generalizable Approach to Learning Optimizers
A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.
Asymmetric self-play for automatic goal discovery in robotic manipulation
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects.
Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models
We further show that the transfer metric can predict the effect of training setups on policy transfer performance.
Solving Rubik's Cube with a Robot Hand
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot.
Learning Dexterous In-Hand Manipulation
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.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
The purpose of this technical report is two-fold.
Asymmetric Actor Critic for Image-Based Robot Learning
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.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
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
Domain Randomization and Generative Models for Robotic Grasping
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.
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL).
Hindsight Experience Replay
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL).
One-Shot Imitation Learning
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.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability.
Extensions and Limitations of the Neural GPU
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).
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world.
Improved Techniques for Training GANs
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
Ranked #14 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
Conditional Image Generation
Semi-Supervised Image Classification
+1
Learning Simple Algorithms from Examples
We present an approach for learning simple algorithms such as copying, multi-digit addition and single digit multiplication directly from examples.
Sequence Level Training with Recurrent Neural Networks
Many natural language processing applications use language models to generate text.
Ranked #14 on
Machine Translation
on IWSLT2015 German-English
Convolutional networks and learning invariant to homogeneous multiplicative scalings
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.
Reinforcement Learning Neural Turing Machines - Revised
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
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.
Ranked #40 on
Machine Translation
on WMT2014 English-French
Learning to Execute
Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used because they are expressive and are easy to train.
Recurrent Neural Network Regularization
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.
Ranked #35 on
Language Modelling
on Penn Treebank (Word Level)
Learning to Discover Efficient Mathematical Identities
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
We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks.
Intriguing properties of neural networks
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks.
Spectral Networks and Locally Connected Networks on Graphs
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.
B-test: A Non-parametric, Low Variance Kernel Two-sample Test
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
A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced.
