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Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.
Hammering towards QED
Jasmin Christian Blanchette, Cezary Kaliszyk, Lawrence C. Paulson, and Josef Urban · 1972
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Automatic acquisition of search guiding heuristics
Christian Suttner and Wolfgang Ertel · 1990
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Learning task-dependent distributed representations by backpropagation through structure
Christoph Goller and Andreas Kuchler · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning from Previous Proof Experience
Jörg Denzinger, Matthias Fuchs, Christoph Goller, and Stephan Schulz · 1999
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Isar - A generic interpretative approach to readable formal proof documents
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Learning search control knowledge for equational deduction , volume 230 of DISKI
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MPTP 0.2: Design, implementation, and initial experiments
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Metamath: A Computer Language for Pure Mathematics
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The TPTP problem library and associated infrastructure
Geoff Sutcliffe · 2009
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Mizar in a nutshell
Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz · 2010
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seL4: formal verification of an operating-system kernel
Gerwin Klein, June Andronick, Kevin Elphinstone, Gernot Heiser, David Cock, Philip Derrin, Dhammika Elkaduwe, Kai Engelhardt, Rafal Kolanski, Michael Norrish, Thomas Sewell, Harvey Tuch, and Simon Winwood · 2010
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Parsing natural scenes and natural language with recursive neural networks
Richard Socher, Cliff C Lin, Chris Manning, and Andrew Y Ng · 2011
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MaLeCoP: Machine learning connection prover
Josef Urban, Jiří Vyskočil, and Petr Štěpánek · 2011
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Automated and Human Proofs in General Mathematics: An Initial Comparison
Jesse Alama, Daniel Kühlwein, and Josef Urban · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Overview and evaluation of premise selection techniques for large theory mathematics
Daniel Kühlwein, Twan van Laarhoven, Evgeni Tsivtsivadze, Josef Urban, and Tom Heskes · 2012
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First-order theorem proving and Vampire
Laura Kovács and Andrei Voronkov · 2013
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System description: E 1.8
Stephan Schulz · 2013
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History of interactive theorem proving
John Harrison, Josef Urban, and Freek Wiedijk · 2014
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A formal proof of the Kepler conjecture
Thomas C. Hales, Mark Adams, Gertrud Bauer, Dat Tat Dang, John Harrison, Truong Le Hoang, Cezary Kaliszyk, Victor Magron, Sean McLaughlin, Thang Tat Nguyen, Truong Quang Nguyen, Tobias Nipkow, Steven Obua, Joseph Pleso, Jason Rute, Alexey Solovyev, An Hoai Thi Ta, Trung Nam Tran, Diep Thi Trieu, Josef Urban, Ky Khac Vu, and Roland Zumkeller · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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FEMaLeCoP: Fairly efficient machine learning connection prover
Cezary Kaliszyk and Josef Urban · 2015
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Reasoning about entailment with neural attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, and Phil Blunsom · 2015
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Going deeper with convolutions
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Yoon Kim · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Structure formation in large theories
Serge Autexier and Dieter Hutter · 2015
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Mizar: State-of-the-art and beyond
Grzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pąk, and Josef Urban · 2015
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Understanding LSTM networks, 2015
Chris Olah · 2015
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning · 2015
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Deepmath-deep sequence models for premise selection
Alexander A Alemi, Francois Chollet, Geoffrey Irving, Niklas Een, Christian Szegedy, and Josef Urban · 2016
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Internal guidance for Satallax
Michael Färber and Chad E. Brown · 2016
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Wavenet: A generative model for raw audio
Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Learning knowledge base inference with neural theorem provers
Tim Rocktäschel and Sebastian Riedel · 2016
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E 1.9.1 User Manual (preliminary version) , 2016
Stephan Schulz · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Holophrasm: a neural automated theorem prover for higher-order logic
Daniel Whalen · 2016
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