57 citations · 71 across the 14 of their papers we have counts for
6 papers · 1 filter
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim +3
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under…
Improving Multi-fidelity Optimization with a Recurring Learning Rate for Hyperparameter Tuning
HyunJae Lee, Gihyeon Lee, Junhwan Kim +3
Despite the evolution of Convolutional Neural Networks (CNNs), their performance is surprisingly dependent on the choice of hyperparameters. However, it remains challenging to effi…
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters
Sung Moon Ko, Sungjun Cho, Dae-Woong Jeong +3
Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task whic…
Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho +1
Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend…
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min +4
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat…
Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning
Sungmin Cha, Sungjun Cho, Dasol Hwang +3
Batch Normalization (BN) and its variants has been extensively studied for neural nets in various computer vision tasks, but relatively little work has been dedicated to studying t…