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1
Intro - GNNs in production
2
How graphs are formed
3
Graph features
4
GNN explained DeepMind GN
5
Different horizons
6
Loss functions
7
Reducing the variance
8
ETA baselines explained
9
How does the inference work
10
Offline results
11
Ablations and experiments
12
Outro, engineering
Description:
Dive into a comprehensive video explanation of the paper "ETA Prediction with Graph Neural Networks in Google Maps." Explore how Google Maps utilizes Graph Neural Networks (GNNs) for real-world applications, including graph formation, feature extraction, and the DeepMind GN model. Learn about different prediction horizons, loss functions, variance reduction techniques, and ETA baselines. Understand the inference process, offline results, and various experiments conducted. Gain insights into the engineering aspects of implementing GNNs in production for accurate travel time estimation.

ETA Prediction with Graph Neural Networks in Google Maps - Paper Explained

Aleksa Gordić - The AI Epiphany
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