Abstract: Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications.
AI medical imaging market is projected to exceed $20B by 2035. Generative models address class imbalances in medical imaging ...
A computational method combining generative AI with atomistic simulations can identify promising platinum alloy catalyst structures for hydrogen fuel cells, report researchers from Science Tokyo.
Personalized course recommendations represent a significant problem in extensive e-learning platforms, as student preferences are in constant flux and educational environments are increasingly ...
DeSCOPE is a single-cell perturbation prediction framework designed for scRNA-seq, scATAC-seq, and general single-cell–level perturbation modeling. It is built on a conditional Variational Autoencoder ...
Additional visualizations highlighting the comparison between the proposed two-stage AG-VQ-VAE network (without skip connections) and the single-stage AG-UNet (with skip connections) are presented.
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 ...
Abstract: We present a variational autoencoder (VAE) learning framework with introspective training for conditional image synthesis, and explore conditional capsule encoder by class-wise mask label ...
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