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Papers
Papers that changed AI, with a plain-language explainer for each.
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- 1998
The Anatomy of a Large-Scale Hypertextual Web Search Engine
A 1998 conference paper by Sergey Brin and Lawrence Page describing PageRank, an algorithm that ranks web pages by the number and quality of links pointing to them, and the search engine built on that algorithm.
- 1995
Support-vector networks
Machine Learning
Corinna Cortes and Vladimir Vapnik's 1995 paper presents a learning machine for two-group classification that maps inputs nonlinearly into a high-dimensional feature space, constructs a linear decision surface, and extends earlier work from separable to non-separable data.
- 1993
Building a Large Annotated Corpus of English: The Penn Treebank
Computational Linguistics
A 1993 journal article by Mitchell P. Marcus, Beatrice Santorini and Mary Ann Marcinkiewicz describing the Penn Treebank: over one million words of Wall Street Journal text annotated with syntactic bracketing, built as a resource for work on English sentence structure.
- 1992
A Training Algorithm for Optimal Margin Classifiers
Published in 1992, this paper by Boser, Guyon and Vapnik presents a training algorithm that maximizes the margin between training patterns and the decision boundary, with experiments on optical character recognition.[^1]
- 1992
Q-learning
Machine Learning
A 1992 technical note by Watkins and Dayan that set out Q-learning, an incremental and computationally light way for an agent to learn optimal action values, and proved it converges to the optimum with probability 1.
- 1985
A Learning Algorithm for Boltzmann Machines
Cognitive Science 9
A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.
- 1948
A Mathematical Theory of Communication
Bell System Technical Journal 27
C. E. Shannon's 1948 paper set out a mathematical framework for communication, defining information in bits, channel capacity, and entropy, and treating messages statistically while setting aside semantic meaning.