Baking Symmetry into GFlowNets

The reduction of a graph to canonical form and the algebra which appears therein

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An Improved Algorithm for Matching Large Graphs

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Janossy Pooling: Learning Deep Permutation-invariant Functions for Variable-size Inputs

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Relational Pooling for Graph Representations

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Distance Encoding—Design Provably More Powerful GNNs for Structural Representation Learning

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ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

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Frame Averaging for Invariant and Equivariant Network Design

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Rotation Invariant Graph Neural Networks using Spin Convolutions

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Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C Lawrence Zitnick · 2021

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Bayesian Structure Learning with Generative Flow Networks

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Biological Sequence Design with GFlowNets

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Gflowout: Dropout with generative flow networks

Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris C. Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, and Yoshua Bengio · 2022

Joint bayesian inference of graphical structure and parameters with a single generative flow network, 2023

Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Nikolay Malkin, Laurent Charlin, and Yoshua Bengio · 2023

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Multi-objective GFlowNets

Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, and Emmanuel Bengio · 2023

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Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

Jiangyan Ma, Yifei Wang, and Yisen Wang · 2023

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GFlowNets and variational inference

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, and Yoshua Bengio · 2023

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The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds

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