arXiv AI
Jun 26
Autoregressive Boltzmann Generators
arXiv:2606. 27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics.
Autoregressive Boltzmann Generators
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction.
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arXiv Machine Learning
Jun 2
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
arXiv:2605. 31498v2 Announce Type: replace Abstract: A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules.
arXiv Machine Learning
Jul 30
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
arXiv:2607. 27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability.
arXiv Machine Learning
Jun 30
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
arXiv:2606. 29110v1 Announce Type: new Abstract: Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations.
arXiv Machine Learning
Jul 20
Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.
arXiv Machine Learning
Jul 24
Boltzmann generators for amorphous particle systems
arXiv:2512. 16607v2 Announce Type: replace-cross Abstract: Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics.
arXiv Machine Learning
Jun 26
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.
Hugging Face Trending Papers
Jul 29
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.
arXiv AI
Aug 12
The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM
arXiv:2505. 11635v2 Announce Type: cross Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous latent models can struggle to express.
arXiv Machine Learning
Jul 23
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
arXiv:2601. 21026v2 Announce Type: replace-cross Abstract: Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.
arXiv Machine Learning
Jun 30
Inference-time optimization for experiment-grounded protein ensemble generation
arXiv:2602. 24007v3 Announce Type: replace-cross Abstract: Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 often fail to produce ensembles that match experimental data.
arXiv Machine Learning
Jul 7
AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).