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Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training.
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Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine. 2016 · 2016
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Summarizing source code using a neural attention model
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What to talk about and how? selective generation using LSTMs with coarse-to-fine alignment
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IntPhys: A framework and benchmark for visual intuitive physics reasoning
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Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang, and Wei Wang. 2018 · 2018
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PHYRE: A new benchmark for physical reasoning
Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, and Ross Girshick. 2019 · 2019
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Hongyuan Mei, Mohit Bansal, and Matthew R. Walter. 2016 · 2016
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Generalizable features from unsupervised learning
Mehdi Mirza, Aaron Courville, and Yoshua Bengio. 2016 · 2016
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Renqiao Zhang, Jiajun Wu, Chengkai Zhang, William T Freeman, and Joshua B Tenenbaum. 2016 · 2016
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Verb physics: Relative physical knowledge of actions and objects
Maxwell Forbes and Yejin Choi. 2017 · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017
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Visual stability prediction and its application to manipulation
Wenbin Li, Ales Leonardis, and Mario Fritz. 2017 · 2017
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Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer. 2017 · 2017
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Do neural language representations learn physical commonsense?
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Reasoning about physical interactions with object-oriented prediction and planning
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The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
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PIQA: Reasoning about physical commonsense in natural language
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