A curated list of awesome neural programming resources, inspired by awesome-computer-vision.

• Workshops or Tutorial
• Related Talks
• Blog Posts
• Papers
• Terminologies

• Neural Abstract Machines & Program Induction (NAMPI) Workshop at NIPS 2016
• Modelling Natural Language, Programs, and their Intersection Tutorial at NAACL 2018

• 2v2 Debate: Interpretability is necessary in machine learning, by Caruana, Simard, Weinberger, LeCun
• Learning to Code: Machine Learning for Program Induction, by ALex Gaunt

• Deep Learning for Program Synthesis, from Microsoft Research

• Neural Programmer-Interpreters, Scott Reed and Nando de Freitas, ICLR16, [ArXiv]
• Making Neural Programming Architectures Generalize via Recursion, Jonathon Cai et al., ICLR17, [Openreview]
• Learning to Represent Programs with Graphs, Miltiadis Allamanis et al., ICLR18, [Openreview], [ArXiv]
• Neural Task Programming: Learning to Generalize Across Hierarchical Tasks, Danfei Xu et al., ICRA18, [ArXiv]

• Neural Program Lattices, Chengtao Li et al., ICLR17, [Openreview]
• Neural GPUs Learn Algorithms, Łukasz Kaiser and Ilya Sutskever, ICLR16, [ArXiv]
• Parametrized Hierarchical Procedures for Neural Programming, Roy Fox et al., ICLR18 [Openreview]

• Neural Scene De-rendering, Jiajun Wu et al., CVPR17, [Paper]

• Programmatically Interpretable Reinforcement Learning, Abhinav Verma et al., ICML18, [ArXiv]

• From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood, Kelvin Guu et al., ACL17 [ArXiv]
• Language to code: Learning semantic parsers for if-this-then-that recipes, C. Quirk et al., ACL15, [paper]

• Neuro-Symbolic Program Synthesis, Emilio Parisotto et al., ICLR17, [ArXiv]
• DeepCoder: Learning to Write Programs, Matej Balog et al., ICLR17, [Openreview] [ArXiv]
• Neural Program Synthesis with Priority Queue Training, Daniel A. Abolafia et al., [Openreview] [ArXiv], [code]
• Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis, Rudy Bunel et al., ICLR18, [Openreview]
• Neural Program Synthesis from Diverse Demonstration Videos, Shao-Hua Sun et al., ICML18, [Project page]

• Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision, Chen Liang et al., ACL17, [ArXiv]
• Using Program Induction to Interpret Transition System Dynamics, Svetlin et al., ICML2017 Workshop

• Neural Program Meta-Induction, Jacob Devlin et al., NIPS17, [ArXiv]
• TerpreT: A Probabilistic Programming Language for Program Induction, Alexander L. Gaunt et al., NIPS16 NAMPI Workshop, [ArXiv]
• Lifelong Perceptual Programming By Example, Alexander L. Gaunt et al., ICLR17 Workshop, [Openreview]
• Dynamic Neural Program Embeddings for Program Repair, Ke Wang, et al. ICLR18, [Openreview]
• Recent Advances in Neural Program Synthesis, Neel Kant

To the extent possible under law, Yuan-Hong Liao has waived all copyright and related or neighboring rights to this work.