- λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ κ΅μ¬ μ€ν°λ
- λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ μμ μ½λ
- μ°Έκ³ μλ£ : Pytorchλ‘ μμνλ λ₯λ¬λ μ λ¬Έ
μ£Όμ°¨ | λ μ§ | λ΄μ© | λ°νμ | λ°ν μλ£ |
---|---|---|---|---|
1 | 22/09/16 | OT | μ΅νκ²½ | π |
2 | 22/09/23 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 1μ₯, 2μ₯ | μ΅νκ²½, μ€μμ§, κΉμμ§ | π |
3 | 22/09/30 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 4μ₯ | μ΄λ€ν, κΉμμ§, λ°λ³΄μ | π |
4 | 22/10/07 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 5μ₯ | λ°μ§μ΄, μ€μ°μ¬, μ΅νκ²½ | π |
5 | 22/10/14 | μ€κ°κ³ μ¬ ν΄μκΈ°κ° | - | - |
6 | 22/10/21 | μ€κ°κ³ μ¬ ν΄μκΈ°κ° | - | - |
7 | 22/10/28 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 6μ₯ | μ΄μμ, μ΄λ€ν, μμν | π |
8 | 22/11/04 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 7μ₯ | μ€μμ§, κΉμμ§, λ°λ³΄μ | π |
9 | 22/11/11 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 8μ₯ | λ°μ§μ΄, μ€μ°μ¬, μ΄μμ | π |
10 | 22/11/18 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 9μ₯ | μ΅νκ²½, μ΄λ€ν, μμν | π |
11 | 22/11/25 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 10μ₯ | μ€μμ§, κΉμμ§, λ°λ³΄μ | π |
12 | 22/12/02 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 12μ₯ | λ°μ§μ΄, μ€μ°μ¬, μ΄μμ | π |
13 | 22/12/09 | κΈ°λ§κ³ μ¬ ν΄μκΈ°κ° | - | - |
14 | 22/12/16 | κΈ°λ§κ³ μ¬ ν΄μκΈ°κ° | - | - |
15 | 22/12/23 | λ₯λ¬λ νμ΄ν μΉ κ΅κ³Όμ 13μ₯ | μ΅νκ²½, μ΄λ€ν, μμν | π |
16 | 22/12/30 | μμ΄λ°μ΄μ | μ 체 | π |
17 | 23/01/06 | νλ‘μ νΈ | - | - |
18 | 23/01/13 | νλ‘μ νΈ | - | - |
19 | 23/01/20 | νλ‘μ νΈ | - | - |
20 | 23/01/27 | νλ‘μ νΈ | - | - |
21 | 23/02/03 | νλ‘μ νΈ | - | - |
22 | 23/02/10 | νλ‘μ νΈ | - | - |
23 | 23/02/17 | νλ‘μ νΈ | - | - |
24] | 23/02/21 | νλ‘μ νΈ | - | - |
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CS224W κ°μ μ€ν°λ
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CS224W κ°μμλ£ Winter 2021 λ²μ
μ£Όμ°¨ | λ μ§ | λ΄μ© | λ°νμ | λ°ν μλ£ |
---|---|---|---|---|
1 | 22/09/16 | OT | μ΄λ€ν | π |
2 | 22/09/23 | 1. Introduction; Machine Learning for Graphs | μ΄λ€ν, μ΅νκ²½ | π |
3 | 22/09/30 | 2. Traditional Methods for ML on Graphs | μ΅μ§μ°, μ΅μμ | π |
4 | 22/10/07 | 3. Node Embeddings | κΉλν, μ΄μλΉ | π |
5 | 22/10/14 | μ€κ°κ³ μ¬ ν΄μκΈ°κ° - λ Όλ¬Έ 리뷰 μ€λΉ | - | π |
6 | 22/10/21 | μ€κ°κ³ μ¬ ν΄μκΈ°κ° - λ Όλ¬Έ 리뷰 μ€λΉ | - | π |
7 | 22/10/28 | Special Session β λ Όλ¬Έ 리뷰 | ALL | π |
8 | 22/11/04 | 4. Link Analysis: PageRank | μ΄λ€ν, μ΅νκ²½ | π |
9 | 22/11/11 | 5. Label Propagation for Node Classification | μ΅μ§μ°, μ΅μμ | π |
10 | 22/11/18 | 6. Graph Neural Networks 1: GNN Model | κΉλν, μ΄μλΉ | π |
11 | 22/11/25 | 7. Graph Neural Networks 2: Design Space | μ΄λ€ν, μ΅νκ²½ | π |
12 | 22/12/02 | 8. Applications of Graph Neural Networks | μ΅μ§μ°, μ΅μμ | π |
13 | 22/12/09 | κΈ°λ§κ³ μ¬ ν΄μκΈ°κ° - λ Όλ¬Έ 리뷰 μ€λΉ | - | π |
14 | 22/12/16 | κΈ°λ§κ³ μ¬ ν΄μκΈ°κ° - λ Όλ¬Έ 리뷰 μ€λΉ | - | π |
15 | 22/12/23 | Special Session β λ Όλ¬Έ 리뷰 | ALL | π |
16 | 22/12/30 | 9. Theory of Graph Neural Networks | κΉλν, μ΄μλΉ | π |
17 | 23/01/06 | 10. Knowledge Graph Embeddings | μ΄λ€ν, μ΅νκ²½ | π |
18 | 23/01/13 | 11. Reasoning over Knowledge Graphs | μ΅μ§μ°, μ΅μμ | π |
19 | 23/01/20 | 12. Frequent Subgraph Mining with GNNs | κΉλν, μ΄μλΉ | π |
20 | 23/01/27 | 13. Community Structure in Networks | μ΄λ€ν, μ΅νκ²½ | π |
21 | 23/02/03 | 14. Traditional Generative Models for Graphs | μ΅μ§μ°, μ΅μμ | π |
22 | 23/02/10 | 15. Deep Generative Models for Graphs | κΉλν, μ΄μλΉ | π |
23 | 23/02/17 | 16. Advanced Topics on GNNs | μ΄λ€ν, μ΅νκ²½, μ΅μ§μ° | π |
24] | 23/02/21 | 17. Scaling Up GNNs | μ΅μμ, κΉλν, μ΄μλΉ | π |