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Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more.
Probability of error of some adaptive pattern-recognition machines
H. Scudder · 1965
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
A new view of the em algorithm that justifies incremental and other variants
Radford M. Neal and Geoffrey E. Hinton · 1993
Earlier work this paper cites.
The “wake-sleep” algorithm for unsupervised neural networks
Geoffrey E Hinton, Peter Dayan, Brendan J Frey, and Radford M Neal · 1995
Earlier work this paper cites.
Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
Earlier work this paper cites.
Statistical parsing with a context-free grammar and word statistics
Eugene Charniak · 1997
Earlier work this paper cites.
Bootstrapping statistical parsers from small datasets
Mark Steedman, Miles Osborne, Anoop Sarkar, Stephen Clark, Rebecca Hwa, Julia Hockenmaier, Paul Ruhlen, Steven Baker, and Jeremiah Crim · 2003
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Effective self-training for parsing
David McClosky, Eugene Charniak, and Mark Johnson · 2006
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Robustfill: Neural program learning under noisy i/o
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
Earlier work this paper cites.
Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao · 2017
Earlier work this paper cites.
Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2017
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Scalable and sustainable deep learning via randomized hashing
Ryan Spring and Anshumali Shrivastava · 2017
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Attention is all you need
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Neural scene de-rendering
Jiajun Wu, Joshua B Tenenbaum, and Pushmeet Kohli · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli · 2018
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Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum · 2018
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Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Drawing early-bird tickets: Towards more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G Baraniuk, Zhangyang Wang, and Yingyan Lin · 2019
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Learning Generative Models of 3D Structures
Siddhartha Chaudhuri, Daniel Ritchie, Jiajun Wu, Kai Xu, and Hao Zhang · 2020
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Dreamcoder: Growing generalizable, interpretable knowledge with wake-sleep bayesian program learning, 2020
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, and Joshua B. Tenenbaum · 2020
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Junxian He, Jiatao Gu, Jiajun Shen, and Marc’Aurelio Ranzato · 2020
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CSGNet: Neural Shape Parser for Constructive Solid Geometry
Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, and Subhransu Maji · 2018
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Neural program synthesis from diverse demonstration videos
Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, and Joseph Lim · 2018
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song · 2019
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Bae-net: Branched autoencoder for shape co-segmentation
Zhiqin Chen, Kangxue Yin, Matthew Fisher, Siddhartha Chaudhuri, and Hao Zhang · 2019
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Write, execute, assess: Program synthesis with a repl
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
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Learning to Describe Scenes with Programs
Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Learning to learn generative programs with memoised wake-sleep, 2020
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Shapeassembly: Learning to generate programs for 3d shape structure synthesis
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Neural shape parsers for constructive solid geometry
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Unsupervised program synthesis for images using tree-structured lstm, 2020
Chenghui Zhou, Chun-Liang Li, and Barnabas Poczos · 2020
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Rethinking pre-training and self-training, 2020
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, and Quoc V. Le · 2020
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Fusion 360 gallery: A dataset and environment for programmatic cad construction from human design sequences
Karl D. D. Willis, Yewen Pu, Jieliang Luo, Hang Chu, Tao Du, Joseph G. Lambourne, Armando Solar-Lezama, and Wojciech Matusik · 2021
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