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20172026
most citedStructured Bayesian Pruning via Log-Normal Multiplicative Noise

131 citations · 179 across the 26 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

The Superposition of Diffusion Models Using the Itô Density Estimator

Marta Skreta, Lazar Atanackovic, Avishek Joey Bose +2

The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurrin…

cs.LG2024★ 2 cited

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

Yuanqi Du, Michael Plainer, Rob Brekelmans +5

Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large sp…

cs.LG2024★ 1 cited

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Lazar Atanackovic, Xi Zhang, Brandon Amos +5

Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical p…

cs.NE2024★ 6 cited

Efficient Evolutionary Search Over Chemical Space with Large Language Models

Haorui Wang, Marta Skreta, Cher-Tian Ser +11

Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutiona…

cs.LG2024

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Lingkai Kong, Yuanqi Du, Wenhao Mu +8

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed…