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 / TaeHoon Lee 

2. GraphSAGE; Inductive Representation Learning on Large Graphs / MyeongSeop Kim

3. GAT; Graph Attention Network / JiHun Kim

4. GIN; How Powerful are Graph Neural Networks? & WL Test / ByungWoo Kim

5. GNN Explainer; Generating Explanations for Graph Neural Networks / JeongA Seol

6. Application1. Spatial Transcriptomics / MyeongSeop Kim

7. Application 2; / ByungWoo Kim

8. Application 3; / JeongA Seol

9. Application 4; / WooKyung Kim

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

Next Coming

Course 2. LLM(VLM) Reasoning

Course 3. Generative Models

Course 4. Reinforcement Learning