Summary of Guided Sketch-based Program Induction by Search Gradients, By Ahmad Ayaz Amin
Guided Sketch-Based Program Induction by Search Gradients
by Ahmad Ayaz Amin
First submitted to arxiv on: 10 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to develop a more sophisticated approach to program induction, which involves capturing an interpretable and generalizable algorithm through training. Traditional methods for program induction are limited in their ability to be applied to various types of tasks, as they tend to be formulated as a single, all-encompassing model parameterized by neural networks. The proposed framework uses search gradients and evolution strategies to learn parameterized programs, allowing programmers to impart task-specific code while still benefiting from accelerated learning through end-to-end gradient-based optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Program induction is a way to solve certain tasks using machine learning. Right now, these methods aren’t very good at solving different types of problems because they try to use one approach that works for everything. The researchers want to make program induction better by allowing programmers to add special instructions to the “program sketch” while still using machines to learn and improve quickly. |
Keywords
* Artificial intelligence * Machine learning * Optimization
