Flux Matching generalizes score-based generative modeling by using a weaker objective that admits infinitely many non-conservative vector fields with the data as stationary distribution, enabling new design choices beyond traditional score matching.
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Density estimation using Real NVP
31 Pith papers cite this work. Polarity classification is still indexing.
31
Pith papers citing it
abstract
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations.
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representative citing papers
Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
Denoising diffusion probabilistic models generate high-quality images by learning to reverse a fixed forward diffusion process, achieving FID 3.17 on CIFAR10.
DriftXpress approximates drifting kernels via projected RKHS fields to lower training cost of one-step generative models while matching original FID scores.
NTM models each generative reverse step as a conditional normalizing flow with a hybrid shallow-deep architecture, enabling exact-likelihood training and strong four-step sampling performance on text-to-image tasks.
Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by observables.
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
MOFAT applied to SN2024ggi shows CO triggering inner SiO formation with a receding edge, order-of-magnitude mass drop, clumping signatures, and no dust formation.
MorphoFlow learns compact probabilistic 3D shape representations from sparse annotations using neural implicits, autodecoders, autoregressive flows, and adaptive sparsity priors on latent dimensions.
VaFES constructs a latent space from reversible collective variables and variationally optimizes a tractable-density generative model to produce a continuous free energy surface from which rare events are directly sampled.
Operator projections of trained sampler functions in 2D phi^4 lattice theory decompose residuals into zero-mode Binder and finite-k correlator components, distinguishing flow-matching, diffusion, and normalizing-flow models.
CONTRA generates sharp multi-dimensional conformal prediction regions by defining nonconformity scores as distances from the center in the latent space of a normalizing flow.
STARFlow2 presents an autoregressive flow-based architecture for unified multimodal text-image generation by interleaving a VLM stream with a TarFlow stream via residual skips and a unified latent space.
A neural network maps ODE states to a slow-evolving latent space with dynamics derived from the original equations via the chain rule, enabling accelerated simulations with fewer function calls.
Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
iTARFlow augments normalizing flows with diffusion-style iterative denoising during sampling while preserving end-to-end likelihood training, reaching competitive results on ImageNet 64/128/256.
OLLM models next-token generation as a latent-indexed set of options, enabling up to 70% math reasoning correctness versus 51% baselines and structure-based alignment via a compact latent policy.
The lookahead drifting model improves upon the drifting model by sequentially computing multiple drifting terms that incorporate higher-order gradient information, leading to better performance on toy examples and CIFAR10.
DSEE is a flow-based emulator that generates stellar evolution tracks and isochrones as probabilistic outputs from a single model trained on millions of simulations, enabling fast interpolation and uncertainty-aware analyses.
citing papers explorer
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Generative Modeling with Flux Matching
Flux Matching generalizes score-based generative modeling by using a weaker objective that admits infinitely many non-conservative vector fields with the data as stationary distribution, enabling new design choices beyond traditional score matching.
-
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Rectified flow learns straight-path neural ODEs for distribution transport, yielding efficient generative models and domain transfers that work well even with a single simulation step.
-
Denoising Diffusion Probabilistic Models
Denoising diffusion probabilistic models generate high-quality images by learning to reverse a fixed forward diffusion process, achieving FID 3.17 on CIFAR10.
-
DriftXpress: Faster Drifting Models via Projected RKHS Fields
DriftXpress approximates drifting kernels via projected RKHS fields to lower training cost of one-step generative models while matching original FID scores.
-
Normalizing Trajectory Models
NTM models each generative reverse step as a conditional normalizing flow with a hybrid shallow-deep architecture, enabling exact-likelihood training and strong four-step sampling performance on text-to-image tasks.
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On the Invariance and Generality of Neural Scaling Laws
Neural scaling laws are invariant under bijective data transformations and change predictably with information resolution ρ under non-bijective transformations, enabling cross-domain transport of fitted exponents.
-
TRACE: Transport Alignment Conformal Prediction via Diffusion and Flow Matching Models
TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
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TMDs in the Lens of Generative AI: A Pixel-Based Approach to Partonic Imaging
A nonparametric pixel-based Bayesian method integrates TMD evolution with generative AI and SVD to image parton distributions and reveal null TMDs unconstrained by observables.
-
Risk-Controlled Post-Processing of Decision Policies
Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.
-
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Diffusion model priors enable training-free Bayesian sampling for more accurate rain field reconstruction from path-integrated commercial microwave link measurements than Gaussian process baselines.
-
Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
NF-NPCDR enhances neural processes with normalizing flows to model personalized multi-interest preferences and uses a preference pool plus adaptive decoder to improve cross-domain recommendations for cold-start users.
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Probing the 3D Structures of Supernovae through IR Signatures of CO and SiO
MOFAT applied to SN2024ggi shows CO triggering inner SiO formation with a receding edge, order-of-magnitude mass drop, clumping signatures, and no dust formation.
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MorphoFlow: Sparse-Supervised Generative Shape Modeling with Adaptive Latent Relevance
MorphoFlow learns compact probabilistic 3D shape representations from sparse annotations using neural implicits, autodecoders, autoregressive flows, and adaptive sparsity priors on latent dimensions.
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Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
VaFES constructs a latent space from reversible collective variables and variationally optimizes a tractable-density generative model to produce a continuous free energy surface from which rare events are directly sampled.
-
Operator Spectroscopy of Trained Lattice Samplers
Operator projections of trained sampler functions in 2D phi^4 lattice theory decompose residuals into zero-mode Binder and finite-k correlator components, distinguishing flow-matching, diffusion, and normalizing-flow models.
-
CONTRA: Conformal Prediction Region via Normalizing Flow Transformation
CONTRA generates sharp multi-dimensional conformal prediction regions by defining nonconformity scores as distances from the center in the latent space of a normalizing flow.
-
STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation
STARFlow2 presents an autoregressive flow-based architecture for unified multimodal text-image generation by interleaving a VLM stream with a TarFlow stream via residual skips and a unified latent space.
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Accelerating the Simulation of Ordinary Differential Equations Through Physics-Preserving Neural Networks
A neural network maps ODE states to a slow-evolving latent space with dynamics derived from the original equations via the chain rule, enabling accelerated simulations with fewer function calls.
-
Conservative Flows: A New Paradigm of Generative Models
Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.
-
Robust Conditional Conformal Prediction via Branched Normalizing Flow
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
-
Normalizing Flows with Iterative Denoising
iTARFlow augments normalizing flows with diffusion-style iterative denoising during sampling while preserving end-to-end likelihood training, reaching competitive results on ImageNet 64/128/256.
-
OLLM: Options-based Large Language Models
OLLM models next-token generation as a latent-indexed set of options, enabling up to 70% math reasoning correctness versus 51% baselines and structure-based alignment via a compact latent policy.
-
Lookahead Drifting Model
The lookahead drifting model improves upon the drifting model by sequentially computing multiple drifting terms that incorporate higher-order gradient information, leading to better performance on toy examples and CIFAR10.
-
Dartmouth Stellar Evolution Emulator (DSEE) 1: Generative Stellar Evolution Model Database
DSEE is a flow-based emulator that generates stellar evolution tracks and isochrones as probabilistic outputs from a single model trained on millions of simulations, enabling fast interpolation and uncertainty-aware analyses.
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Jeffreys Flow: Robust Boltzmann Generators for Rare Event Sampling via Parallel Tempering Distillation
Jeffreys Flow distills Parallel Tempering trajectories via Jeffreys divergence to produce robust Boltzmann generators that suppress mode collapse and correct sampling inaccuracies for rare event sampling.
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Multivariate Time Series Anomaly Detection via Dual-Branch Reconstruction and Autoregressive Flow-based Residual Density Estimation
DBR-AF decouples cross-variable correlations in reconstruction and applies autoregressive flows to model residual densities for improved anomaly detection in multivariate time series.
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VideoGPT: Video Generation using VQ-VAE and Transformers
VideoGPT generates competitive natural videos by learning discrete latents with VQ-VAE and modeling them autoregressively with a transformer.
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To Use AI as Dice of Possibilities with Timing Computation
Proposes verb-based paradigm with timing computation to enable data-driven discovery of patient trajectories and counterfactual timing from EHR data without domain knowledge.
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Pre-localization of Massive Black Hole Binaries in the Millihertz Band
A neural spline flow pipeline performs amortized inference on millihertz MBHB signals, delivering ~20 deg² pre-merger sky localizations in ~1 minute while matching PTMCMC sky modes and parameter uncertainties.
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Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks
Invertible Neural Networks are used to generate gas turbine combustor designs that meet specified performance criteria from a training database of parameterized designs and simulations.
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Scalable DDPM-Polycube: An Extended Diffusion-Based Method for Hexahedral Mesh and Volumetric Spline Construction
Scalable DDPM-Polycube adds a blind-hole cube primitive, enlarges the grid to 3D, and introduces genus-guided hierarchical verification to improve diffusion-based polycube generation for complex geometries.