A curated list of resources about generative flow networks (GFlowNets).

• Monograph & Tutorial
• Paper
• Workshop paper & Note

GFlowNet Foundations
Yoshua Bengio, et al.

GFlowNets for AI-Driven Scientific Discovery [review paper]
Moksh Jain, et al.

The GFlowNet Tutorial
Yoshua Bengio.

GFlowNet Tutorial (Colab)
Emmanuel Bengio.

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks [GFlowNet for Bayesian dynamical causal discovery]
Lazar Atanackovic, et al. [code]

Stochastic Generative Flow Networks [model-based GFlowNets for stochastic transitions]
Ling Pan, et al.

GFlowNet-EM for Learning Compositional Latent Variable Models [GFlowNet for latent posterior]
Edward Hu, et al. [code]

Distributional GFlowNets with Quantile Flows [distributional GFlowNets for stochastic rewards]
Dinghuai Zhang, et al.

Better Training of GFlowNets with Local Credit and Incomplete Trajectories [forward-looking GFlowNet]
Ling Pan, et al.

Unifying Generative Models with GFlowNets and Beyond
Dinghuai Zhang, et al. ICML 2022 Beyond Bayes workshop.

A theory of continuous generative flow networks [GFlowNet on continuous space]
Salem Lahlou, et al. [code]

Multi-Objective GFlowNets
Moksh Jain, et al. [code]

Learning GFlowNets from partial episodes for improved convergence and stability [SubTB criterion]
Kanika Madan, et al. [code]

Generative Augmented Flow Networks [enabling intermediate rewards]
Ling Pan, et al. ICLR 2023 spotlight. [code]

GFlowNets and variational inference
Nikolay Malkin, et al. ICLR 2023. [code]

Trajectory Balance: Improved Credit Assignment in GFlowNets [trajectory balance (TB) criterion]
Nikolay Malkin, et al. NeurIPS 2022. [code]

Bayesian Structure Learning with Generative Flow Networks [causal graph Bayesian posterior]
Tristan Deleu, et al. UAI 2022. [code]

Generative Flow Networks for Discrete Probabilistic Modeling [energy-based GFlowNet]
Dinghuai Zhang, et al. ICML 2022. [code]

Biological Sequence Design with GFlowNets
Moksh Jain, et al. ICML 2022. [code]

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation [first GFlowNet paper]
Emmanuel Bengio, et al. NeurIPS 2021. [blog post] [code]

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes [DAG-GFlowNet with parameters]
Mizu Nishikawa-Toomey, et al.

Evaluating Generalization in GFlowNets for Molecule Design
Andrei Cristian Nica, et al. ICML 2022 MLDD workshop.

If you have any suggestion or want to add your own work, please feel free to drop a message to [email protected].