publications

2020

1. Language Models are Few-Shot Learners

   Tom B. Brown, Benjamin Mann, Nick Ryder, and 8 more authors

   arXiv preprint arXiv:2005.14165, 2020

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   Introduced GPT-3, demonstrating that large language models can perform new tasks from just a few examples without explicit training.

   @article{brown2020language,
     title = {Language Models are Few-Shot Learners},
     author = {Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
     journal = {arXiv preprint arXiv:2005.14165},
     year = {2020},
   }

2018

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

   Jacob Devlin, Ming-Wei Chang, Kenton Lee, and 1 more author

   arXiv preprint arXiv:1810.04805, 2018

   Abs Bib

   Introduced BERT, which achieved state-of-the-art results on multiple NLP tasks through bidirectional context and transfer learning.

   @article{devlin2018bert,
     title = {BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding},
     author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
     journal = {arXiv preprint arXiv:1810.04805},
     year = {2018},
   }

2017

1. Attention Is All You Need

   Ashish Vaswani, Noam Shazeer, Niki Parmar, and 5 more authors

   In Advances in Neural Information Processing Systems, 2017

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   Introduced the Transformer architecture, which revolutionized natural language processing and became the foundation for models like BERT and GPT.

   @inproceedings{vaswani2017attention,
     title = {Attention Is All You Need},
     author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Łukasz and Polosukhin, Illia},
     booktitle = {Advances in Neural Information Processing Systems},
     volume = {30},
     pages = {5998--6008},
     year = {2017},
   }

2016

1. Mastering the Game of Go with Deep Neural Networks and Tree Search

   David Silver, Aja Huang, Chris J. Maddison, and 8 more authors

   Nature, 2016

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   Described AlphaGo, the first computer program to defeat a world champion at the game of Go.

   @article{silver2016mastering,
     title = {Mastering the Game of Go with Deep Neural Networks and Tree Search},
     author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and Van Den Driessche, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and others},
     journal = {Nature},
     volume = {529},
     number = {7587},
     pages = {484--489},
     year = {2016},
     publisher = {Nature Publishing Group},
     doi = {10.1038/nature16961},
   }

2015

1. Human-level Control Through Deep Reinforcement Learning

   Volodymyr Mnih, Koray Kavukcuoglu, David Silver, and 8 more authors

   Nature, 2015

   Abs DOI Bib

   Introduced Deep Q-Networks (DQN), which combined deep learning with reinforcement learning to achieve human-level performance on Atari games.

   @article{mnih2015human,
     title = {Human-level Control Through Deep Reinforcement Learning},
     author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and others},
     journal = {Nature},
     volume = {518},
     number = {7540},
     pages = {529--533},
     year = {2015},
     publisher = {Nature Publishing Group},
     doi = {10.1038/nature14236},
   }

2012

1. ImageNet Classification with Deep Convolutional Neural Networks

   Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton

   In Advances in Neural Information Processing Systems, 2012

   Awarded Abs Bib

   Breakthrough in deep learning for computer vision

   This paper introduced AlexNet, which dramatically improved image classification performance and launched the deep learning revolution.

   @inproceedings{krizhevsky2012imagenet,
     title = {ImageNet Classification with Deep Convolutional Neural Networks},
     author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
     booktitle = {Advances in Neural Information Processing Systems},
     volume = {25},
     pages = {1097--1105},
     year = {2012},
   }

2009

1. The Quest for Artificial Intelligence: A History of Ideas and Achievements

   Nils J. Nilsson

   In , 2009

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   Comprehensive history of artificial intelligence by one of the pioneers in the field.

   @inproceedings{nilsson2009quest,
     title = {The Quest for Artificial Intelligence: A History of Ideas and Achievements},
     author = {Nilsson, Nils J.},
     booktitle = {},
     year = {2009},
     publisher = {Cambridge University Press},
   }

1998

1. Gradient-Based Learning Applied to Document Recognition

   Yann LeCun, Léon Bottou, Yoshua Bengio, and 1 more author

   Proceedings of the IEEE, 1998

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   Introduced LeNet-5, a pioneering convolutional neural network architecture for handwritten digit recognition.

   @article{lecun1998gradient,
     title = {Gradient-Based Learning Applied to Document Recognition},
     author = {LeCun, Yann and Bottou, Léon and Bengio, Yoshua and Haffner, Patrick},
     journal = {Proceedings of the IEEE},
     volume = {86},
     number = {11},
     pages = {2278--2324},
     year = {1998},
     publisher = {IEEE},
     doi = {10.1109/5.726791},
   }

1997

1. Long Short-Term Memory

   Sepp Hochreiter, and Jürgen Schmidhuber

   Neural Computation, 1997

   Abs DOI Bib

   Introduced LSTM networks, solving the vanishing gradient problem and enabling effective learning in recurrent neural networks.

   @article{hochreiter1997long,
     title = {Long Short-Term Memory},
     author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
     journal = {Neural Computation},
     volume = {9},
     number = {8},
     pages = {1735--1780},
     year = {1997},
     publisher = {MIT Press},
     doi = {10.1162/neco.1997.9.8.1735},
   }

1986

1. Learning Representations by Back-propagating Errors

   David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams

   Nature, 1986

   Abs DOI Bib

   Popularized the backpropagation algorithm for training neural networks, leading to a resurgence in neural network research.

   @article{rumelhart1986learning,
     title = {Learning Representations by Back-propagating Errors},
     author = {Rumelhart, David E. and Hinton, Geoffrey E. and Williams, Ronald J.},
     journal = {Nature},
     volume = {323},
     pages = {533--536},
     year = {1986},
     doi = {10.1038/323533a0},
   }

1982

1. Neural Networks and Physical Systems with Emergent Collective Computational Abilities

   John J. Hopfield

   Proceedings of the National Academy of Sciences, 1982

   Abs DOI Bib

   Introduced Hopfield networks, which showed how associative memory could be implemented in neural networks.

   @article{hopfield1982neural,
     title = {Neural Networks and Physical Systems with Emergent Collective Computational Abilities},
     author = {Hopfield, John J.},
     journal = {Proceedings of the National Academy of Sciences},
     volume = {79},
     number = {8},
     pages = {2554--2558},
     year = {1982},
     publisher = {National Academy of Sciences},
     doi = {10.1073/pnas.79.8.2554}
   }

1969

1. Perceptrons: An Introduction to Computational Geometry

   Marvin Minsky, and Seymour Papert

   1969

   Abs Bib

   This influential work highlighted limitations of single-layer perceptrons, contributing to the first "AI winter" by demonstrating what simple neural networks could not compute.

   @article{minsky1969perceptrons,
     title = {Perceptrons: An Introduction to Computational Geometry},
     author = {Minsky, Marvin and Papert, Seymour},
     journal = {},
     year = {1969},
     publisher = {MIT Press}
   }

1959

1. Some Studies in Machine Learning Using the Game of Checkers

   Arthur L. Samuel

   IBM Journal of Research and Development, 1959

   Abs DOI Bib

   Described one of the first self-learning programs and introduced the term "machine learning" to the field.

   @article{samuel1959some,
     title = {Some Studies in Machine Learning Using the Game of Checkers},
     author = {Samuel, Arthur L.},
     journal = {IBM Journal of Research and Development},
     volume = {3},
     number = {3},
     pages = {210--229},
     year = {1959},
     publisher = {IBM},
     doi = {10.1147/rd.33.0210},
   }

1958

1. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain

   Frank Rosenblatt

   Psychological Review, 1958

   Abs DOI Bib

   Introduced the perceptron, one of the first computational models of a neuron capable of learning, laying groundwork for neural networks.

   @article{rosenblatt1958perceptron,
     title = {The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain},
     author = {Rosenblatt, Frank},
     journal = {Psychological Review},
     volume = {65},
     number = {6},
     pages = {386--408},
     year = {1958},
     publisher = {American Psychological Association},
     doi = {10.1037/h0042519},
   }

1956

1. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence

   John McCarthy, Marvin L. Minsky, Nathan Rochester, and 1 more author

   In , 1956

   Awarded Abs Bib

   Birth of the field of artificial intelligence

   This proposal for the Dartmouth Conference coined the term "Artificial Intelligence" and is considered the founding document of AI as a field.

   @inproceedings{mccarthy1956dartmouth,
     title = {A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence},
     author = {McCarthy, John and Minsky, Marvin L. and Rochester, Nathan and Shannon, Claude E.},
     booktitle = {},
     year = {1956},
   }

1950

1. Computing Machinery and Intelligence

   Alan M. Turing

   Mind, 1950

   Awarded Abs DOI Bib

   Foundational paper in artificial intelligence

   This seminal paper introduced what is now known as the "Turing Test" for machine intelligence, proposing that a machine could be considered intelligent if its responses were indistinguishable from a human’s.

   @article{turing1950computing,
     title = {Computing Machinery and Intelligence},
     author = {Turing, Alan M.},
     journal = {Mind},
     volume = {59},
     number = {236},
     pages = {433--460},
     year = {1950},
     publisher = {Oxford University Press},
     doi = {10.1093/mind/LIX.236.433},
   }

1943

1. A Logical Calculus of the Ideas Immanent in Nervous Activity

   Warren S. McCulloch, and Walter Pitts

   The Bulletin of Mathematical Biophysics, 1943

   Abs DOI Bib

   This pioneering work proposed the first mathematical model of a neural network, showing how simple computational units could perform complex logical operations.

   @article{mcculloch1943logical,
     title = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
     author = {McCulloch, Warren S. and Pitts, Walter},
     journal = {The Bulletin of Mathematical Biophysics},
     volume = {5},
     number = {4},
     pages = {115--133},
     year = {1943},
     publisher = {Springer},
     doi = {10.1007/BF02478259},
   }