Neural Transport Nested Sampling

arXiv:2609.29413 · cs.LG, stat.ML · Submitted 2026-09-24 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/yallup/ntns

Terminology

• Flow Matching — 19× in this paper · explained in 8 episodes
• International Conference on Machine Learning — 14× in this paper
• Benchmark — 13× in this paper · explained in 2 episodes
• Normalizing Flow — 7× in this paper · explained in 3 episodes
• Sequential Monte Carlo — 7× in this paper
• Neural Transport Nested Sampling — 6× in this paper
• Normalizing Flows — 6× in this paper · explained in 4 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Flow Matching Model — 4× in this paper · explained in 2 episodes
• Nested Slice Sampling — 4× in this paper
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Phase Transitions — 3× in this paper · explained in 2 episodes
• 2-Wasserstein Distance — 2× in this paper · explained in 1 episode
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Iterated Denoising Energy Matching — 2× in this paper
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Markov-chain Monte Carlo — 1× in this paper
• Neural Surrogates — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Sequential Monte Carlo (SMC) — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• A foundation model for atomistic materials chemistry
• Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching
• Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
• NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
• Fast protein backbone generation with SE(3) flow matching
• Weak Poincar'e inequality comparisons for ideal and hybrid slice sampling
• BlackJAX: Composable Bayesian inference in JAX
• Isambard-AI: a leadership class supercomputer optimised specifically for Artificial Intelligence

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