Open Seminar

Course 1. GNN and Mamba

https://docs.google.com/spreadsheets/d/1eKBB_uq3RFhTCJDWD3AHzJW230Nmtl5G_gIwKD03dXc/edit?usp=sharing

1.  그래프의 기본 성질과 GCN; Graph Convolution Network (이태훈) 

2. GraphSAGE; Inductive Representation Learning on Large Graphs (김명섭)

3. GAT; Graph Attention Network (김지훈)

4. GIN; How Powerful are Graph Neural Networks? & WL Test (김병우)

5. GNN Explainer; Generating Explanations for Graph Neural Networks (설정아)

6. Application 1; Cell/Tissue Graph (김병우)

7. Application 2; Scene Graph (김명섭)

8. Application 3; EGG Brain Connectivity Graph (설정아)

9. Application 4; Clinical Knowledge Graph (김지훈)

1. State Space Model

2. Structered State Space for Sequence Modeling

3. Mamba 1: Selective State Space

4. Mamba 2: Hardware-aware Algorithm & Mamba Architecture

5. Mamba for signal & Time Series

6. Vision Mamba; Efficient Visual Representation Learning with Bidirectional State Space Model