Главная
Study mode:
on
1
Dynamic graphs
2
Suboptimal strategies
3
Terminology, temporal neighborhood
4
High-level overview of the system
5
We need to go deeper
6
Using temporal information to sample
7
Information leakage and the solution
8
Main modules explained
9
Memory staleness problem
10
Temporal graph attention
11
Vector representation of time
12
Batch size tradeoff
13
Results and ablation studies
14
Recap of the system
15
Some confusing parts
Description:
Dive deep into the world of Temporal Graph Networks (TGN) with this comprehensive video explanation. Explore dynamic graphs, learn how to obtain vectorized representations of time, and uncover the intricate details behind the TGN paper. Gain insights into suboptimal strategies, temporal neighborhoods, and the high-level system overview. Discover solutions to information leakage, understand main modules, and tackle the memory staleness problem. Delve into temporal graph attention, vector representation of time, and batch size tradeoffs. Analyze results, ablation studies, and recap the entire system. Address confusing aspects and enhance your understanding of this advanced topic in graph machine learning.

Temporal Graph Networks - GNN Paper Explained

Aleksa Gordić - The AI Epiphany
Add to list
0:00 / 0:00