Presentation title - miltos allamanis · Akka.NET AutoMapper BenchmarkDotNet BotBuilder choco commandlinet CommonMark.NET Dapper EntityFramework Hangfire Humanizer Leant Nancy Newtonsoft.Json

https://arxiv.org/abs/1711.00740

Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. (2015).

Gated graph sequence neural networks. arXiv preprint

arXiv:1511.05493.

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Li et al (2015). Gated graph sequence neural networks.

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Li et al (2015). Gated graph sequence neural networks.

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Li et al (2015). Gated graph sequence neural networks.

Li et al (2015). Gated graph sequence neural networks.

• node selection

• node classification

• graph classification

https://arxiv.org/abs/1711.00740

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Implementation

• Sparse TensorFlow implementation

• 16 edge types (forward + backward)

• ~900 nodes/graph ~8k edges/graph

Stats

• 55 graphs/s during training

• 219 graphs/s during testing

Accuracy (%) Local Model Avg BiRNN GGNN

Seen Projects 15.8 73.5 82.1

Unseen Projects 13.8 59.7 68.6

3.8 type-correct alternative variables per slot (median 3, σ= 2.6)

bool string string out string

var

while null

if return true

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return false

bool string string out string

var

while null

if return true

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return false

https://ml4code.github.io