2.3k citations · 2.3k across the 4 of their papers we have counts for
4 papers
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation
Rodrigo Castellon, Achintya Gopal, Brian Bloniarz +1
The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive r…
EDGE: Editable Dance Generation From Music
Jonathan Tseng, Rodrigo Castellon, C. Karen Liu
Dance is an important human art form, but creating new dances can be difficult and time-consuming. In this work, we introduce Editable Dance GEneration (EDGE), a state-of-the-art m…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Codified audio language modeling learns useful representations for music information retrieval
Rodrigo Castellon, Chris Donahue, Percy Liang
We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explor…