22 citations · 35 across the 8 of their papers we have counts for
5 papers · 1 filter
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning
Gaichao Li, Jinsong Chen, Kun He
By incorporating the graph structural information into Transformers, graph Transformers have exhibited promising performance for graph representation learning in recent years. Exis…
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning
Guodong Liu, Tongling Wang, Shuoxi Zhang +1
Model-Agnostic Meta-Learning (MAML) is a famous few-shot learning method that has inspired many follow-up efforts, such as ANIL and BOIL. However, as an inductive method, MAML is u…
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li +1
The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers trea…
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
Fadime Sener, Dibyadip Chatterjee, Daniel Shelepov +4
Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instruc…
Adaptive Channel Allocation for Robust Differentiable Architecture Search
Chao Li, Jia Ning, Han Hu +1
Differentiable ARchiTecture Search (DARTS) has attracted much attention due to its simplicity and significant improvement in efficiency. However, the excessive accumulation of the…