Author

Mark B. Gerstein

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#machine learning Preprint Oct 2026

Learning Disentangled Representations with Quantum Variational Autoencoders

Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery of interpretable and disentangled factors. Such representations can facilitate the interpretation and controllable generation of data describing complex scientific system...

Gao-Yuan Wang, Jerry Tan, M. Gerstein · 0 citations

#generative ai Nov 2026

DreamFold: A World Model to efficiently generate protein folding pathways in the latent space.

While there is an abundance of static data for the structure of biological macromolecules, the data regarding their folding mechanisms and dynamics is scarce, posing a challenge to the training of AI models. The World Model approach, which has been very successful in robotics, can come in handy in this regard. From lim...

Alan Ianeselli, J. Im, Eddie Cavallin et al. · Protein Science · 0 citations