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Found 28 papers, 18 papers with code
Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
In the first phase, we collect 2. 5 hours of play data, which is used to train self-supervised tactile encoders.
Navigating to Objects in the Real World
In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
Holo-Dex: Teaching Dexterity with Immersive Mixed Reality
A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot.
CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory
We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization.
TorchAudio: Building Blocks for Audio and Speech Processing
This document describes version 0. 10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain.
droidlet: modular, heterogenous, multi-modal agents
In recent years, there have been significant advances in building end-to-end Machine Learning (ML) systems that learn at scale.
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both.
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.
Automatic Differentiation in PyTorch
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.
Wasserstein Generative Adversarial Networks
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Training Language Models Using Target-Propagation
While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps.
Transformation-Based Models of Video Sequences
In this work we propose a simple unsupervised approach for next frame prediction in video.
Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Semantic Segmentation using Adversarial Networks
Adversarial training has been shown to produce state of the art results for generative image modeling.
TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks
We consider scenarios from the real-time strategy game StarCraft as new benchmarks for reinforcement learning algorithms.
Discovering Causal Signals in Images
Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.
A MultiPath Network for Object Detection
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
Ranked #101 on
Instance Segmentation
on COCO test-dev
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative model capable of producing high quality samples of natural images.
MazeBase: A Sandbox for Learning from Games
This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #9 on
Image Clustering
on Tiny-ImageNet
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.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative parametric model capable of producing high quality samples of natural images.
A mathematical motivation for complex-valued convolutional networks
Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.
Fast Convolutional Nets With fbfft: A GPU Performance Evaluation
We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units.
Pedestrian Detection with Unsupervised Multi-Stage Feature Learning
Pedestrian detection is a problem of considerable practical interest.
Convolutional Neural Networks Applied to House Numbers Digit Classification
We classify digits of real-world house numbers using convolutional neural networks (ConvNets).
