2.2k citations · 4k across the 30 of their papers we have counts for
8 papers · 1 filter
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea +12
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly u…
Variable-rate discrete representation learning
Sander Dieleman, Charlie Nash, Jesse Engel +1
Semantically meaningful information content in perceptual signals is usually unevenly distributed. In speech signals for example, there are often many silences, and the speed of pr…
DDSP: Differentiable Digital Signal Processing
Jesse Engel, Lamtharn Hantrakul, Chenjie Gu +1
Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, a…
Latent Translation: Crossing Modalities by Bridging Generative Models
Yingtao Tian, Jesse Engel
End-to-end optimization has achieved state-of-the-art performance on many specific problems, but there is no straight-forward way to combine pretrained models for new problems. Her…
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam Roberts, Jesse Engel, Colin Raffel +2
The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen l…
Learning via social awareness: Improving a deep generative sketching model with facial feedback
Natasha Jaques, Jennifer McCleary, Jesse Engel +4
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards.…