Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs

von Kim, Jinwoo
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 33 of 36 (2022)

High-Fidelity Synthesis with Disentangled Representation

von Lee, Wonkwang
Veröffentlicht in ECCV (16. : 2020 : Online) Computer vision - ECCV 2020 ; Part 26 (2020)

Equivariant Hypergraph Neural Networks

von Kim, Jinwoo
Veröffentlicht in ECCV (17. : 2022 : Tel Aviv; Online) Computer vision – ECCV 2022 ; Part 21 (2022)

Multi-View Representation Learning via Total Correlation Objective

von Hwang, HyeongJoo
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 15 of 36 (2022)

VARIATIONAL INTERACTION INFORMATION MAXIMIZATION FOR CROSS-DOMAIN DISENTANGLEMENT

von Hwang, Hyeongjoo
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 27 of 27 (2021)

Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost

von Cho, Sungjun
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 32 of 50 (2023)

Pure Transformers are Powerful Graph Learners

von Kim, Jinwoo
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 19 of 50 (2023)