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[CS330] Introduction to Meta Learning
Meta Learning, Few-shot Learning
2021๋
5์ 26์ผ
ยท
0๊ฐ์ ๋๊ธ
[XAI] SHAP (SHapley Additive exPlanation)
Shapley Value, Additive Feature Attribution Method, SHAP
2021๋
4์ 28์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 12. Network Effects and Cascading Behavior
12. Decision based Diffusion Model
2021๋
4์ 5์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 9. Graph Neural Networks: Hands-on Session
09. GNN code : Pytorch Geometric
2021๋
3์ 8์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 8. Graph Neural Networks
08. Graph Neural Network (GNN), Graph Convolutional Network (GCN), Graph Attention Network (GAT)
2021๋
3์ 5์ผ
ยท
2๊ฐ์ ๋๊ธ
[CS224W] 7. Graph Representation Learning
07. Node Embedding, Random Walk, transE
2021๋
3์ 4์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 6. Message Passing and Node Classification
06. Relational Classification, Iterative Classification, Belief Propagation
2021๋
2์ 26์ผ
ยท
0๊ฐ์ ๋๊ธ.png?w=768)
[CS224W] 5. Spectral Clustering
05. Graph Partition, Spectral Clustering, Motif-Based Spectral Clustering
2021๋
2์ 21์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 4. Community Structure in Networks
04. Network Community, Modularity, Overlapping Community
2021๋
2์ 15์ผ
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[CS224W] 3. Motifs and Structural Roles in Networks
03. Subgraph, Motifs, Graphlets
2021๋
2์ 13์ผ
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0๊ฐ์ ๋๊ธ
[CS224W] 2. Properties of Networks and Random Graph Models
02. gnp, smallworld, kronecker graph
2021๋
2์ 10์ผ
ยท
1๊ฐ์ ๋๊ธ
[CS224n] Lecture 4 - Backpropagation and Computation Graph
์์ฑ์ : ํฌ๋น ์ค 13๊ธฐ ์ด์ฌ๋น
2020๋
12์ 17์ผ
ยท
0๊ฐ์ ๋๊ธ