DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

Yannic Kilcher · Beginner ·📐 ML Fundamentals ·5y ago
#dreamcoder #programsynthesis #symbolicreasoning Classic Machine Learning struggles with few-shot generalization for tasks where humans can easily generalize from just a handful of examples, for example sorting a list of numbers. Humans do this by coming up with a short program, or algorithm, that explains the few data points in a compact way. DreamCoder emulates this by using neural guided search over a language of primitives, a library, that it builds up over time. By doing this, it can iteratively construct more and more complex programs by building on its own abstractions and therefore solve more and more difficult tasks in a few-shot manner by generating very short programs that solve the few given datapoints. The resulting system can not only generalize quickly but also delivers an explainable solution to its problems in form of a modular and hierarchical learned library. Combining this with classic Deep Learning for low-level perception is a very promising future direction. OUTLINE: 0:00 - Intro & Overview 4:55 - DreamCoder System Architecture 9:00 - Wake Phase: Neural Guided Search 19:15 - Abstraction Phase: Extending the Internal Library 24:30 - Dreaming Phase: Training Neural Search on Fictional Programs and Replays 30:55 - Abstraction by Compressing Program Refactorings 32:40 - Experimental Results on LOGO Drawings 39:00 - Ablation Studies 39:50 - Re-Discovering Physical Laws 42:25 - Discovering Recursive Programming Algorithms 44:20 - Conclusions & Discussion Paper: https://arxiv.org/abs/2006.08381 Code: https://github.com/ellisk42/ec Abstract: Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks t
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Chapters (11)

Intro & Overview
4:55 DreamCoder System Architecture
9:00 Wake Phase: Neural Guided Search
19:15 Abstraction Phase: Extending the Internal Library
24:30 Dreaming Phase: Training Neural Search on Fictional Programs and Replays
30:55 Abstraction by Compressing Program Refactorings
32:40 Experimental Results on LOGO Drawings
39:00 Ablation Studies
39:50 Re-Discovering Physical Laws
42:25 Discovering Recursive Programming Algorithms
44:20 Conclusions & Discussion
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