15 citations · 30 across the 12 of their papers we have counts for
12 papers
Topological Flow Matching
Kacper Wyrwal, İsmail İlkan Ceylan, Alexander Tong
Flow matching is a powerful generative modeling framework, valued for its simplicity and strong empirical performance. However, its standard formulation treats signals on structure…
Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers
Bruno Trentini, Dejan Stancevic, Michael M. Bronstein +2
For a fixed flow-based generative model under a small inference budget, sample quality can depend strongly on where the sampler spends its few function evaluations. Flow matching a…
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
Accurately predicting experimentally realizable 3D molecular crystal structures from their 2D chemical graphs is a long-standing open challenge in computational chemistry called cr…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
Planner Aware Path Learning in Diffusion Language Models Training
Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks +5
Diffusion language models have emerged as a powerful alternative to autoregressive models, enabling fast inference through more flexible and parallel generation paths. This flexibi…
Scalable Equilibrium Sampling with Sequential Boltzmann Generators
Charlie B. Tan, Avishek Joey Bose, Chen Lin +3
Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators tackle this problem by pairing normaliz…
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng +9
Generative modeling of discrete data underlies important applications spanning text-based agents like ChatGPT to the design of the very building blocks of life in protein sequences…
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Guillaume Huguet, James Vuckovic, Kilian Fatras +9
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences…
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper Kapuśniak, Peter Potaptchik, Teodora Reu +5
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Des…
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose +9
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem…
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Danqi Liao, Chen Liu, Benjamin W. Christensen +6
Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to compute reliably in high dimensions. Indeed, i…
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet +7
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…