arXiv Machine Learning
Aug 6
Stable GFlowNets with TV Monitoring and Probabilistic Guarantees
arXiv:2605. 01729v2 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample states proportional to an unnormalized reward.
GFlowNets and variational inference
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arXiv AI
Jun 16
Proximal Policy Optimization for Amortized Discrete Sampling
arXiv:2606. 15793v1 Announce Type: cross Abstract: This paper explores policy gradient algorithms for training stochastic policies to sample from structured discrete probability distributions under the Generative Flow Network (GFlowNet) framework.
arXiv Machine Learning
4d ago
Trajectory balance: Improved credit assignment in GFlowNets
The paper introduces a new learning objective called trajectory balance for Generative Flow Networks (GFlowNets), aiming to improve credit assignment across long action sequences. It demonstrates that minimizing this objective yields a policy that samples exactly from the target distribution. Experiments on four domains show that trajectory balance enhances convergence, sample diversity, and robustness to long sequences and large action spaces.
arXiv Machine Learning
Jun 5
Your GFlowNet Secretly Learns an Optimal Transport Plan
arXiv:2606. 06272v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) are a framework for sampling structured objects via stochastic trajectories in a directed graph.
arXiv Machine Learning
3d ago
Amortizing intractable inference in large language models
arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...
arXiv Machine Learning
Aug 5
Information-Geometric Forward Policy Training in GFlowNets
arXiv:2608. 03967v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) have emerged as a flexible framework for amortised inference over discrete and mixed discrete-continuous objects, requiring only an unnormalised target density specified through a reward.
arXiv Machine Learning
Aug 6
GFlowNet Training by Policy Gradients
arXiv:2408. 05885v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties.
arXiv Machine Learning
Jun 2
Flow-Transformed Implicit Processes for Function-Space Variational Inference
arXiv:2606. 01954v1 Announce Type: new Abstract: Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling.
arXiv Machine Learning
4d ago
Improved off-policy training of diffusion samplers
The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.
arXiv Machine Learning
Jul 21
Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602. 21565v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial.
arXiv Machine Learning
3d ago
Delta-AI: Local objectives for amortized inference in sparse graphical models
arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\D...
arXiv AI
Jun 2
Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior
arXiv:2606. 02453v1 Announce Type: cross Abstract: Despite the remarkable fidelity of generative models, they frequently suffer from mode collapse.