Rosicast’s documentation¶
I have no intention to break the copyright. I just want to share a great contents from others and organize those to make easy to use. if there are issue, please contact me via email or github. Thanks.
Links
Libraries
Datasets
Notation
CS224w_2021_fall
PyTorch_Geometric
- 1. Introduction: Hands-on Graph Neural Networks
- 2. Node Classification with Graph Neural Networks
- 3. Graph Classification with Graph Neural Networks
- 4. Scaling Graph Neural Networks
- 5. Point Cloud Classification with Graph Neural Networks
- 6. Explaining GNN Model Predictions using Captum
- 7. Customizing Aggregations within Message Passing with
torch_geometric.nn.aggr - 8. Node Classification with W&B
Pyg_Tutorial_Project
- Tutorial 1: Introduction
- Tutorial 2: PyTorch basics
- Tutorial 3: GAT implementation
- Tutorial 4: Convolutional Layers - Spectral methods
- Tutorial 5: Aggregation
- Tutorial 6: GAE & VGAE
- Tutorial 7: ARGA & ARVGA
- Tutorial 9_1: Recurrent GNNs
- Tutorial 9_2: Recurrent GNNs
- Tutorial 11: DeepWalk and node2vec - Implementation details
- Tutorial 12: GAE for link prediction
- Tutorial 13: Node2Vec for link prediction
- Tutorial 14: Data Handling in PyG (Part 1)
- Tutorial 15: Data Handling in PyG (Part 2)
Category_theory
AMMI_ex
Notes
- Chemprop
- Comparison of One-Hot Encoding and Embedding Layer
- Cumulative Density Functions (CDFs)
- Gumbel Distribution and Gumbel-Softmax
- Kullback-Leibler Divergence
- Lec_7_23 Shift operator
- Lie algebras and Lie groups
- Pooling Architecture Search
- Tanimoto Similarity Explained
- The sources of error
- One label per class
- Scaffold-based Split
- Stratified Split
- Supernet