group theory

Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups NeurIPS 2022 A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups ICML 2021 Equivariant Networks for Pixelized Spheres ICML 2021 Provably Strict Generalisation Benefit for Equivariant Models ICML 2021 Scalars are universal: Equivariant machine learning, structured like classical physics NeurIPS 2021 Self-Supervised Learning Disentangled Group Representation as Feature NeurIPS 2021 A Group-Theoretic Framework for Data Augmentation NeurIPS 2020 A Group-Theoretic Framework for Data Augmentation JMLR 2020 Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data ICLR 2020 Equivariant Networks for Hierarchical Structures NeurIPS 2020 PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions ICML 2020 Symmetry and Group in Attribute-Object Compositions CVPR 2020 Universal Equivariant Multilayer Perceptrons ICML 2020 A General Theory of Equivariant CNNs on Homogeneous Spaces NeurIPS 2019 Relation Embedding with Dihedral Group in Knowledge Graph ACL 2019 Symmetry-Based Disentangled Representation Learning requires Interaction with Environments NeurIPS 2019 Group Invariance Principles for Causal Generative Models AISTATS 2018 On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups ICML 2018 Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM AISTATS 2015 Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014 Fully Decentralized Task Swaps with Optimized Local Searching RSS 2014 Fast Resampling Weighted v-Statistics NeurIPS 2012 Multiresolution analysis on the symmetric group NeurIPS 2012 On Invariance in Hierarchical Models NeurIPS 2009 Characteristic Kernels on Groups and Semigroups NeurIPS 2008