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Found 15 papers, 7 papers with code
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires strategic planning over long time horizons with real-time low-level execution.
Vector Quantized Models for Planning
Our key insight is to use discrete autoencoders to capture the multiple possible effects of an action in a stochastic environment.
Broaden Your Views for Self-Supervised Video Learning
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data.
Predicting Video with VQVAE
In recent years, the task of video prediction-forecasting future video given past video frames-has attracted attention in the research community.
Ranked #10 on
Video Prediction
on Kinetics-600 12 frames, 64x64
Low Bit-Rate Speech Coding with VQ-VAE and a WaveNet Decoder
In order to efficiently transmit and store speech signals, speech codecs create a minimally redundant representation of the input signal which is then decoded at the receiver with the best possible perceptual quality.
Visual Imitation with a Minimal Adversary
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
Unsupervised speech representation learning using WaveNet autoencoders
We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms.
Preventing Posterior Collapse with delta-VAEs
Due to the phenomenon of "posterior collapse," current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data.
Ranked #7 on
Image Generation
on ImageNet 32x32
(bpd metric)
Sample Efficient Adaptive Text-to-Speech
Instead, the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
The challenge of realistic music generation: modelling raw audio at scale
It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations.
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet.
Parallel Multiscale Autoregressive Density Estimation
Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images.
Ranked #2 on
Image Compression
on ImageNet32
A note on the evaluation of generative models
In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional.
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition.
Ranked #1 on
Gesture Recognition
on Montalbano
