Awesome GFlowNets 
A curated list of resources about generative flow networks (GFlowNets).
Table of Contents
Monograph & Tutorial
GFlowNet Foundations [theoretical framework]
Yoshua Bengio, et al.
GFlowNets for AI-Driven Scientific Discovery [review paper]
Moksh Jain, et al.
The GFlowNet Tutorial [high level introduction]
Yoshua Bengio.
GFlowNet Tutorial [PRACTICAL colab notebook]
Emmanuel Bengio.
Paper
Generative Flow Networks as Entropy-Regularized RL
Daniil Tiapkin, et al. [code]
Order-Preserving GFlowNets
Yihang Chen, et al.
PhyloGFN: Phylogenetic inference with generative flow networks
Mingyang Zhou, et al.
Pre-Training and Fine-Tuning Generative Flow Networks
Ling Pan, et al.
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization [practical continuous space sampler]
Dinghuai Zhang, et al. [code]
Amortizing intractable inference in large language models [finetune LLMs as GFlowNets]
Edward J. Hu, et al. [code]
Local Search GFlowNets
Minsu Kim, et al. [code]
Delta-AI: Local objectives for amortized inference in sparse graphical models
Jean-Pierre Falet, et al. [code]
Expected flow networks in stochastic environments and two-player zero-sum games
Marco Jiralerspong, et al. [code]
Compositional Sculpting of Iterative Generative Processes
Timur Garipov, et al. NeurIPS 2023. [code]
Multi-Fidelity Active Learning with GFlowNets
Alex Hernandez-Garcia, et al. [code]
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
Tristan Deleu, et al. NeurIPS 2023. [code]
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang, et al. NeurIPS 2023 spotlight. [code]
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Yiheng Zhu, et al. NeurIPS 2023.
DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks [GFlowNet for Bayesian dynamical causal discovery]
Lazar Atanackovic, et al. NeurIPS 2023. [code]
Stochastic Generative Flow Networks [model-based GFlowNets for stochastic transitions]
Ling Pan, et al. UAI 2023 spotlight. [code]
GFlowNet-EM for Learning Compositional Latent Variable Models [GFlowNet for latent posterior]
Edward J. Hu, et al. ICML 2023. [code]
Distributional GFlowNets with Quantile Flows [distributional GFlowNets for stochastic rewards]
Dinghuai Zhang, et al. [code]
Towards Understanding and Improving GFlowNet Training
Max W. Shen, et al. ICML 2023. [code]
Better Training of GFlowNets with
Local Credit and Incomplete Trajectories [forward-looking GFlowNet]
Ling Pan, et al. ICML 2023. [code]
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. ICML 2023. [code]
Multi-Objective GFlowNets
Moksh Jain, et al. ICML 2023. [code]
Learning GFlowNets from partial episodes for improved convergence and stability [SubTB criterion]
Kanika Madan, et al. ICML 2023 oral. [code]
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu, et al. ICML 2023.
Generative Augmented Flow Networks [enabling intermediate rewards]
Ling Pan, et al. ICLR 2023 spotlight. [code]
Robust Scheduling with GFlowNets
David Wei Zhang, et al. ICLR 2023.
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]
Workshop paper & Note
DGFN: Double Generative Flow Networks
Elaine Lau, et al. NeurIPS 2023 Workshop on Generative AI and Biology (GenBio 2023).
GFN-SR: Symbolic Regression with Generative Flow Networks
Sida Li, et al. NeurIPS 2023 AI for Science Workshop.
Crystal-GFN: sampling crystals with desirable properties and constraints
Alex Hernandez-Garcia, et al. [code]
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems
Trang Nguyen, et al.
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries
Zarif Ikram, et al.
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets
Nikhil Vemgal, et al. ICML 2023 SPIGM workshop.
Generative Flow Networks: a Markov Chain Perspective [merge the initial and the terminal states]
Tristan Deleu, et al.
Thompson sampling for improved exploration in GFlowNets
Jarrid Rector-Brooks, et al. ICML 2023 SPIGM workshop.
BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A. Malik, et al. ICML 2023 SPIGM workshop. [code]
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design
Julien Roy, et al.
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.
Contact
If you have any suggestion or want to add your own work, please feel free to drop a message to dinghuai.zhang@mila.quebec.