Name
Abstract | ||
|---|---|---|
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation. Here, for the first time, we directly compare both approaches on a large-scale, real-world learning task. We additionally contrast to rule-based program synthesis, which uses hand-crafted semantics to guide the program generation. Our neural models use a modified attention RNN to allow encoding of variable-sized sets of I/O pairs. Our best synthesis model achieves 92% accuracy on a real-world test set, compared to the 34% accuracy of the previous best neural synthesis approach. The synthesis model also outperforms a comparable induction model on this task, but we more importantly demonstrate that the strength of each approach is highly dependent on the evaluation metric and end-user application. Finally, we show that we can train our neural models to remain very robust to the type of noise expected in real-world data (e.g., typos), while a highly-engineered rule-based system fails entirely. |
Year | Venue | DocType |
|---|---|---|
2017 | ICML | Conference |
Volume | Citations | PageRank |
abs/1703.07469 | 28 | 0.92 |
References | Authors | |
12 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacob Devlin | 1 | 738 | 32.34 |
| Jonathan Uesato | 2 | 85 | 6.60 |
| Surya Bhupatiraju | 3 | 42 | 3.59 |
| Rishabh Singh | 4 | 684 | 48.19 |
| Abdel-rahman Mohamed | 5 | 3772 | 266.13 |
| Pushmeet Kohli | 6 | 7398 | 332.84 |