This is a list of important publications in computer science, organized by field. Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic Breakthrough – A publication that changed scientific knowledge significantly Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of computer science.
Artificial intelligence
Computing Machinery and Intelligence
Alan Turing
Mind, 59:433–460, 1950. Online copy
Description: This paper discusses the various arguments on why a machine can not be intelligent and asserts that none of those arguments are convincing. The paper also suggested the Turing test, which it calls "The Imitation Game" as according to Turing it is pointless to ask whether or not a machine can think intelligently, and checking if it can act intelligently is sufficient.
A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence
John McCarthy
Marvin Minsky
N. Rochester
C.E. Shannon
Online copy
Description: This summer research proposal inaugurated and defined the field. It contains the first use of the term artificial intelligence and this succinct description of the philosophical foundation of the field: "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." (See philosophy of AI) The proposal invited researchers to the Dartmouth conference, which is widely considered the "birth of AI". (See history of AI.)
Fuzzy sets
Lotfi Zadeh
Information and Control, Vol. 8, pp. 338–353. (1965).
Description: The seminal paper published in 1965 provides details on the mathematics of fuzzy set theory.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
Publisher: Morgan Kaufmann Pub, 1988
Description: This book introduced Bayesian methods to AI.
Artificial Intelligence: A Modern Approach
Stuart J. Russell and Peter Norvig
Prentice Hall, Englewood Cliffs, New Jersey, 1995, Textbook's website
Description: The standard textbook in Artificial Intelligence. The book web site lists over 1100 colleges.
Machine learning
An Inductive Inference Machine
Ray Solomonoff
IRE Convention Record, Section on Information Theory, Part 2, pp. 56–62, 1957
(A longer version of this, a privately circulated report, 1956, is online).
Description: The first paper written on machine learning. Emphasized the importance of training sequences, and the use of parts of previous solutions to problems in constructing trial solutions to new problems.
Language identification in the limit
E. Mark Gold
Information and Control, 10(5):447–474, 1967
Online version: (HTML) (PDF)
Description: This paper created Algorithmic learning theory.
On the uniform convergence of relative frequencies of events to their probabilities V. Vapnik, A. Chervonenkis Theory of Probability and Its Applications, 16(2):264—280, 1971
Description: Computational learning theory, VC theory, statistical uniform convergence and the VC dimension. While the bounds in this paper are not the best possible, it would take 50 years before the best bound was obtained by Michael Naaman in 2021.
A theory of the learnable
Leslie Valiant
Communications of the ACM, 27(11):1134–1142 (1984)
Description: The Probably approximately correct learning (PAC learning) framework.
Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton and Ronald J. Williams
Nature, 323, 533–536, 1986
Description: Seppo Linnainmaa's reverse mode of automatic differentiation (first applied to neural networks by Paul Werbos) is used in experiments by David Rumelhart, Geoff Hinton and Ronald J. Williams to learn internal representations.
Induction of Decision Trees
J.R. Quinlan
Machine Learning, 1. 81—106, 1986.
Description: Decision Trees are a common learning algorithm and a decision representation tool. Development of decision trees was done by many researchers in many areas, even before this paper. Though this paper is one of the most influential in the field.
Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm Nick Littlestone Machine Learning 2: 285–318, 1988 Online version(PDF)