Deep Learning

Solving the 2D Ising model using GANs

I recently posted a new preprint titled Energy-Tweedie: Score meets Score, Energy meets Energy on ArXiv.

I recently helped present our work (together with Prof. Gašper Beguš, paper) on the usage of deep generative models for discovering the building blocks of sperm whale communication at the Decoding Communication in Nonhuman Species symposium hosted at the Simons Institute for the Theory of Computing, UC Berkeley:

We have a pre-print out based on our work with Project CETI, an initiative to decipher sperm whale communication using machine learning. In the paper, we combine approaches that help discover how known properties of human language are learned by generative models when trained on labeled speech audio data with methodology inspired by causal inference and apply it to the communication system of sperm whales, for which we do not have any such ground truth.

A brief introduction to diffusion models

Given that the semester’s over, I have finally gotten around to updating the site. We have a pre-print out based on the work I did during my internship last summer. It examines the problem of inferring the characteristics of different people involved in the process of translating and reviewing text by using Hierarchical Bayesian Models.