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Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers.
How to Solve It
George Pólya · 1945
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Eugene Wigner · 1960
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A formal definition of intelligence based on an intensional variant of algorithmic complexity
Jose Hernández-Orallo · 1998
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S. Legg and Marcus Hutter · 2007
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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How well do computers solve math word problems? large-scale dataset construction and evaluation
D. Huang, Shuming Shi, Chin-Yew Lin, J. Yin, and W. Ma · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and P. Blunsom · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, L. Kaiser, and Illia Polosukhin · 2017
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 2019
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Holist: An environment for machine learning of higher-order theorem proving (extended version)
K. Bansal, S. Loos, Markus N. Rabe, Christian Szegedy, and S. Wilcox · 2019
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Improving graph neural network representations of logical formulae with subgraph pooling
Maxwell Crouse, I. Abdelaziz, Cristina Cornelio, Veronika Thost, L. Wu, Kenneth D. Forbus, and Achille Fokoue · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Gamepad: A learning environment for theorem proving
Daniel Huang, Prafulla Dhariwal, D. Song, and Ilya Sutskever · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Analysing mathematical reasoning abilities of neural models
D. Saxton, Edward Grefenstette, Felix Hill, and P. Kohli · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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LogiQA: A challenge dataset for machine reading comprehension with logical reasoning
J. Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
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MathZero, the classification problem, and set-theoretic type theory
David A. McAllester · 2020
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A diverse corpus for evaluating and developing english math word problem solvers
Shen-Yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2020
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Expbert: Representation engineering with natural language explanations
Shikhar Murty, Pang Wei Koh, and Percy Liang · 2020
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Generative language modeling for automated theorem proving
Stanislas Polu and Ilya Sutskever · 2020
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T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, T. Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith · 2020
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Deberta: Decoding-enhanced bert with disentangled attention
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Scaling laws for autoregressive generative modeling
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Scaling laws for neural language models
J. Kaplan, Sam McCandlish, T. Henighan, T. Brown, Benjamin Chess, R. Child, Scott Gray, A. Radford, Jeffrey Wu, and Dario Amodei · 2020
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Unifiedqa: Crossing format boundaries with a single qa system, 2020
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Hannaneh Hajishirzi · 2020
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Deep learning for symbolic mathematics
Guillaume Lample and Franccois Charton · 2020
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Mathematical reasoning via self-supervised skip-tree training
Markus N. Rabe, Dennis Lee, K. Bansal, and Christian Szegedy · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, M. Matena, Yanqi Zhou, W. Li, and Peter J. Liu · 2020
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A promising path towards autoformalization and general artificial intelligence
Christian Szegedy · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Program synthesis with large language models
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Measuring massive multitask language understanding
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Lime: Learning inductive bias for primitives of mathematical reasoning
Yuhuai Wu, M. Rabe, Wenda Li, Jimmy Ba, Roger B. Grosse, and Christian Szegedy · 2021
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