Rodrigo Castellon

TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.

TL;DR: The Editable Dance GEneration (EDGE) as discussed by the authors uses a transformer-based diffusion model paired with Jukebox, a strong music feature extractor, and confers powerful editing capabilities well-suited to dance, including joint-wise conditioning, and in-betweening.

TL;DR: In this paper, the authors explore representations from Jukebox (Dhariwal et al. 2020), a music generation system containing a language model trained on codified audio from 1M songs.

TL;DR: Differentially private TaBular AutoRegressive Transformer (DP-TBART) as mentioned in this paper is a transformer-based autoregressive model that maintains differential privacy and achieves performance competitive with marginal-based methods on a wide variety of datasets, capable of even outperforming state-of-theart methods in certain settings.