Abstract Information in proteins flows from sequence to structure to function, with each step
causally driven by the preceding one. Protein design is founded on inverting this process …

Developing a generative model of realistic order flow in financial markets is a challenging
open problem, with numerous applications for market participants. Addressing this, we …

Achieving a balance between image quality (precision) and diversity (recall) is a significant
challenge in the domain of generative models. Current state-of-the-art models primarily rely …

The zero-sum game in generative adversarial networks (GANs) for learning the distribution
of observed data is known to reduce to the minimization of a divergence measure between …

Generative modeling is a widely-used machine learning method with various applications in
scientific and industrial fields. Its primary objective is to simulate new examples drawn from …

In recent years, large language models (LLMs) have shown exceptional capabilities across
various natural language processing (NLP) tasks. However, such impressive performance …

The Langevin Dynamics framework, which aims to generate samples from the score function
of a probability distribution, is widely used for analyzing and interpreting score-based …

Variational Autoencoders (VAEs) have been a pioneering force in the realm of deep
generative models. Amongst its legions of progenies, Wasserstein Autoencoders (WAEs) …

Generative modeling have become an essential tool in machine learning for generating
realistic samples from complex data distributions. Despite significant advancements in …

This paper explores the problem of generative modeling, aiming to simulate diverse
examples from an unknown distribution based on observed examples. While recent studies …