Nanowerk

CatDRX: a novel AI model for discovery of chemical catalysts

Researchers develop an AI-based platform that integrates reaction data with catalyst performance for the design of new ...
Mirage News

CatDRX: AI Model Revolutionizes Catalyst Discovery

CatDRX is a generative AI framework developed at Institute of Science Tokyo, which enables the design of new chemical ...
Hosted on MSN

Novel AI platform accelerates discovery of chemical catalysts for industry

CatDRX is a generative AI framework developed at Institute of Science Tokyo, which enables the design of new chemical catalysts based on the specific chemical reactions in which they are used. The ...
Business Wire

Variational AI Announces Oversubscribed $5.5 Million Financing to Launch Foundation Model for Small Molecule Drug Discovery

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

Merck inks $349M biobucks deal to apply AI to challenging targets

Merck & Co. has doubled down on its partnership with Variational AI, striking a deal worth up to $349 million to collaborate on small molecule candidates against two targets. Variational disclosed a ...
IEEE

Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation

Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...
Frontiers

Thyroid nodule segmentation in ultrasound images using transformer models with masked autoencoder pre-training

Introduction: Thyroid nodule segmentation in ultrasound (US) images is a valuable yet challenging task, playing a critical role in diagnosing thyroid cancer. The difficulty arises from factors such as ...
INSPIRE

Quantum Error Mitigation via Autoencoder Neural Networks

Quantum computing has gained significant attention in recent years, with numerous algorithms and applications under active development. Limited by the current quantum ...
Frontiers

Graph-informed convolutional autoencoder to classify brain responses during sleep

1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran 2 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom ...
INSPIRE

Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning

Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical ...
marktechpost

This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost Alternative to End-to-End Sparse Autoencoder Training for Interpretability

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...
IEEE

Tree Variational Autoencoder for Code

Abstract: Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder ...