Published in NeurIPS, 2024
We analysed the efficiency and expressiveness of invariant and equivariant networks from a canonization perspective.
Recommended citation: George Ma, Yifei Wang, Derek Lim, Stefanie Jegelka, Yisen Wang (2024). A Canonization Perspective on Invariant and Equivariant Learning. In Thirty-eighth Conference on Neural Information Processing Systems. https://arxiv.org/abs/2405.18378
Published in NeurIPS-AI4Science, 2023
We proposed to incorporate state and action symmetries into GFlowNets.
Recommended citation: Jiangyan Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang (2023). Baking Symmetry into GFlowNets. In NeurIPS 2023 AI for Science: from Theory to Practice. https://openreview.net/forum?id=CZGHAeeBk3
Published in NeurIPS, 2023
We designed the Laplacian Canonization algorithm to address the sign and basis ambiguities of Laplacian eigenvectors.
Recommended citation: Jiangyan Ma, Yifei Wang, Yisen Wang (2023). Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=1mAYtdoYw6