Research map / Large language models / Language, safety and interpretability
Convolutional Neural Networks over Tree Structures for Programming Language Processing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add coding layer (ablation) | TBCNN · 104-class program classification (validation) | validation accuracy: 92.3 -> 94.4 | Helped |
| use continuous binary tree instead of CBoW child averaging (ablation) | TBCNN · 104-class program classification (validation) | validation accuracy: 93.1 -> 94.4 | Helped |
| use 3-way pooling instead of 1-way (ablation) | TBCNN · 104-class program classification (validation) | validation accuracy: 94.4 -> 94.3 | No clear effect |
| add unsupervised pretraining of AST node embeddings (ablation) | TBCNN · 104-class program classification (OJ) | accuracy | No clear effect |
| add unsupervised pretraining of AST node embeddings (ablation) | TBCNN · 104-class program classification (OJ) | training speed: nearly $1/3$ | Helped |
| tree-based convolution vs recursive NN | program classification · 104-class program classification (OJ) | test accuracy: 84.8 -> 94.0 | Helped |
| TBCNN vs RBF SVM with bag-of-trees | bubble sort detection · bubble sort detection | accuracy: 77.1 -> 89.1 | Helped |
Closest papers by meaning
- A Grammar-Based Structural CNN Decoder for Code Generation (AAAI 2019 · similarity 0.89)
- TreeCaps: Tree-Based Capsule Networks for Source Code Processing (AAAI 2021 · similarity 0.88)
- Tree-to-tree Neural Networks for Program Translation (NeurIPS 2018 · similarity 0.88)
- TreeGen: A Tree-Based Transformer Architecture for Code Generation (AAAI 2020 · similarity 0.86)
- Learning to Represent Programs with Graphs (ICLR 2018 · similarity 0.85)
- Diffusion On Syntax Trees For Program Synthesis (ICLR 2025 · similarity 0.84)
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