FUND: Density Flow for Sampling Unnormalised Distributions

arXiv:2609.32296 · cs.LG, hep-lat · Submitted 2026-09-26 · Read on arXiv

cs.LG, hep-lat

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Benchmark — 19× in this paper · explained in 2 episodes
• Mode Collapse — 12× in this paper · explained in 2 episodes
• Flow Matching — 7× in this paper · explained in 8 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Fidelity — 6× in this paper · explained in 2 episodes
• Normalizing Flow — 6× in this paper · explained in 3 episodes
• Annealed Importance Sampling — 5× in this paper
• Generative Model — 5× in this paper · explained in 2 episodes
• Generative Models — 5× in this paper · explained in 2 episodes
• Normalizing Flows — 5× in this paper · explained in 4 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Markov Chain Monte Carlo — 4× in this paper
• Catastrophic Forgetting — 3× in this paper · explained in 5 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Continual Learning — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Flow Annealed Importance Sampling Bootstrap — 2× in this paper
• KL Regularization — 2× in this paper · explained in 2 episodes
• Negative Log-Likelihood — 2× in this paper
• Optimal Transport — 2× in this paper · explained in 5 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Effective Sample Size — 1× in this paper
• Embeddings — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Iterated Denoising Energy Matching — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions
• Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks