Bernoulli Flow Models: Self-Consistent Generative Modeling for Binary Data
cs.LG, cs.CV
Submitted: 2026-10-08
Updated: 2026-10-08
Terminology
Sources
- Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
- $\alpha$-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models
- Prediction--Loss Alignment for Sampler--Robust Flow Matching Training
- Flow Matching for Generative Modeling
- Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
- DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
- Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion
- Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions
- Progressive Distillation for Fast Sampling of Diffusion Models
- CoVAE: Consistency Training of Variational Autoencoders
- Consistency Models
- Score-Based Generative Modeling through Stochastic Differential Equations
- LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
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