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Maksym Korablyov

3 accepted papers

2024

SE(3)-Stochastic Flow Matching for Protein Backbone Generation

ICLR 2024spotlight

The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce \foldflow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions---…

2023

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability

ICML 2023oral

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfully used for various probabilistic modeling tasks. Existing training objectives for GFlowNets are either local to states o…

2021

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

NeurIPS 2021poster

This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions, such that the probability of generating an object is proportional to a given positive reward for that object. Whereas standard return maximization tends to co…