Abstract: Deep generative models, such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), are widely used in collaborative filtering. They usually learn users’ preferences ...

Abstract: In recent years, Variational Autoencoders (VAEs) have attracted considerable interest due to their capability to generate high-fidelity data while safeguarding user privacy through ...

Looking ahead: Recent advancements in scientific discovery methodologies are using AI and emerging quantum technologies to compress decades of materials research into months or weeks. While these ...

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

VANCOUVER, British Columbia--(BUSINESS WIRE)--Variational AI, the company behind Enki™, an advanced foundation model for small molecule drug discovery, today ...

Variational AI, Inc., a generative AI drug discovery company, today announced a collaboration with Merck, known as MSD outside of the United States and Canada, to apply Variational AI’s Enki™ platform ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...

This repository contains the code and models used in the paper "Understanding European Heatwaves with Variational Autoencoders" submitted to Earth System Dynamics ...

Neural networks are designed to learn compressed representations of high-dimensional data, and autoencoders (AEs) are a widely-used example of such models. These systems employ an encoder-decoder ...

Background: The rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology, accelerating timelines from years to months.

ABSTRACT: Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...

Generative Modeling is a branch of machine learning that focuses on creating models representing distributions of data, denoted as $P(X)$. $X$ represents the data ...