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| Name | Deep Learning Foundations: Xinyun Chen 's talk on "Learning-Based Program Synthesis" |
| Video Uploader | Video From Soheil Feizi |
| Upload Date | This Video Uploaded At 17-10-2022 13:48:57 |
| Video Discription | Course webpage: http://www.cs.umd.edu/class/fall2022/cmsc828W/ With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this talk, I will present my work on learning-based program synthesis, where we have developed deep learning techniques to handle 3 core challenges of program synthesis: input ambiguity, program complexity, and generalization. The talk will have three parts: (1) learning to synthesize programs from multi-modal and potentially ambiguous specifications in the wild; (2) learning with execution for solving challenging programming problems; and (3) compositional generalization via program learning. Readings: 1. SpreadsheetCoder: Formula Prediction from Semi-structured Context: https://arxiv.org/pdf/2106.15339.pdf 2. Execution-Guided Neural Program Synthesis: https://openreview.net/pdf?id=H1gfOiAqYm 3. Compositional Generalization via Neural-Symbolic Stack Machines: https://arxiv.org/pdf/2008.06662.pdf 4. (additional) Competition-Level Code Generation with AlphaCode: https://arxiv.org/pdf/2203.07814.pdf 5. (additional) Compositional Semantic Parsing with Large Language Models: https://arxiv.org/pdf/2209.15003.pdf |
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