Graph Convolutions Enrich the Self-Attention in Transformers!

Graph Convolutions Enrich the Self-Attention in Transformers!

7 December 2023

Jeongwhan Choi

Nathaniel Trask

Papers citing "Graph Convolutions Enrich the Self-Attention in Transformers!"

8 / 8 papers shown

Title
Setting the Record Straight on Transformer Oversmoothing G. Dovonon M. Bronstein Matt J. Kusner 20 5 0 09 Jan 2024
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation Jeongwhan Choi Hyowon Wi C. Lee Sung-Bae Cho Dongha Lee Noseong Park DiffM 28 2 0 27 Dec 2023
Stabilizing Transformer Training by Preventing Attention Entropy Collapse Shuangfei Zhai Tatiana Likhomanenko Etai Littwin Dan Busbridge Jason Ramapuram Yizhe Zhang Jiatao Gu J. Susskind AAML 38 64 0 11 Mar 2023
A Simple Yet Effective SVD-GCN for Directed Graphs Chunya Zou Andi Han Lequan Lin Junbin Gao GNN 45 7 0 19 May 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq R. Joty S. Hoi 201 1,451 0 02 Sep 2021
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 181 907 0 02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 226 1,726 0 09 Jun 2018
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 244 35,884 0 25 Aug 2016