Search Results for author:
Found 27 papers, 19 papers with code
CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning
CURL extracts high level features from raw pixels using a contrastive learning objective and performs off-policy control on top of the extracted features.
Decision Transformer: Reinforcement Learning via Sequence Modeling
In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
Ranked #43 on
Atari Games
on Atari 2600 Pong
(using extra training data)
VideoGPT: Video Generation using VQ-VAE and Transformers
We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50.
Ranked #200 on
Image Classification
on ImageNet
Revisiting ResNets: Improved Training and Scaling Strategies
Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1. 7x - 2. 7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet.
Ranked #1 on
Document Image Classification
on AIP
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations.
Ranked #33 on
Atari Games
on Atari 2600 Amidar
Bottleneck Transformers for Visual Recognition
Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.
Ranked #50 on
Instance Segmentation
on COCO minival
Reinforcement Learning with Latent Flow
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates
Furthermore, since our weighted Bellman backups rely on maintaining an ensemble, we investigate how weighted Bellman backups interact with other benefits previously derived from ensembles: (a) Bootstrap; (b) UCB Exploration.
VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers
We present VideoGen: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.
R-LAtte: Attention Module for Visual Control via Reinforcement Learning
Attention mechanisms are generic inductive biases that have played a critical role in improving the state-of-the-art in supervised learning, unsupervised pre-training and generative modeling for multiple domains including vision, language and speech.
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay
In this paper, we present Latent Vector Experience Replay (LeVER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements without sacrificing the performance of RL agents.
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3. 6 mask AP on rare categories.
Ranked #1 on
Object Detection
on PASCAL VOC 2007
D2RL: Deep Dense Architectures in Reinforcement Learning
While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored.
SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning
A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations.
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Off-policy deep reinforcement learning (RL) has been successful in a range of challenging domains.
Reinforcement Learning with Augmented Data
To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms.
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.
Ranked #1 on
Continuous Control
on Finger, spin (DMControl500k)
PatchFormer: A neural architecture for self-supervised representation learning on images
In this paper, we propose a neural architecture for self-supervised representation learning on raw images called the PatchFormer which learns to model spatial dependencies across patches in a raw image.
Data-Efficient Image Recognition with Contrastive Predictive Coding
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge.
Ranked #6 on
Contrastive Learning
on imagenet-1k
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with efficient sampling and inference.
Ranked #13 on
Image Generation
on ImageNet 32x32
(bpd metric)
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization.
Universal Planning Networks
We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it.
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states.
Dynamic Frame skip Deep Q Network
Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain.
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task.
