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Found 22 papers, 9 papers with code
SoundStorm: Efficient Parallel Audio Generation
We present SoundStorm, a model for efficient, non-autoregressive audio generation.
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning.
Continuous Control with Action Quantization from Demonstrations
The proposed approach consists in learning a discretization of continuous action spaces from human demonstrations.
What Matters for Adversarial Imitation Learning?
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Hyperparameter Selection for Imitation Learning
The vast literature in imitation learning mostly considers this reward function to be available for HP selection, but this is not a realistic setting.
Acme: A Research Framework for Distributed Reinforcement Learning
These implementations serve both as a validation of our design decisions as well as an important contribution to reproducibility in RL research.
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
MULEX: Disentangling Exploitation from Exploration in Deep RL
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour.
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation.
Episodic Curiosity through Reachability
One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning.
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation.
Competitive Training of Mixtures of Independent Deep Generative Models
A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure.
Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
Online Hyper-Parameter Optimization
We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters.
Toward Optimal Run Racing: Application to Deep Learning Calibration
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand.
Critical Hyper-Parameters: No Random, No Cry
The selection of hyper-parameters is critical in Deep Learning.
Better Text Understanding Through Image-To-Text Transfer
Generic text embeddings are successfully used in a variety of tasks.
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Full Resolution Image Compression with Recurrent Neural Networks
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Variable Rate Image Compression with Recurrent Neural Networks
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements.
