A curated list of resources about generative flow networks (GFlowNets).
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.
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]
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 Hu, et al. ICML 2023. [code]
Distributional GFlowNets with Quantile Flows [distributional GFlowNets for stochastic rewards]
Dinghuai Zhang, et al. [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]
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]
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.
If you have any suggestion or want to add your own work, please feel free to drop a message to dinghuai.zhang@mila.quebec.