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Gérard Weisbuch

dblp:66/2202 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 2Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 100%
Databases, data mining, and information retrieval
1 paper
Database system architecture and tuning · 100%
Artificial intelligence
1 paper
Learning theory · 50% Motion planning and robot control · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning
associative memory		 0.011987
Random Iterations of Threshold Networks and Associative Memory · SIAM J. Comput. 1987
Emerging computing paradigms
neuromorphic computing		 0.011987
Random Iterations of Threshold Networks and Associative Memory · SIAM J. Comput. 1987
Emerging computing paradigms › neural computing
threshold gate networks		 0.011987
Random Iterations of Threshold Networks and Associative Memory · SIAM J. Comput. 1987
Robotics › Motion planning and robot control › nonlinear dynamics
attractor dynamics		 0.011987
Random Iterations of Threshold Networks and Associative Memory · SIAM J. Comput. 1987
Machine learning › Learning theory › neural network theory
network dynamics		 0.011987
Random Iterations of Threshold Networks and Associative Memory · SIAM J. Comput. 1987

Methods — techniques the papers use, named apart from their topics

energy function · 0.0scaling laws · 0.0scaling law · 0.0
YearPublicationVenuePosition
2001 Book Review: Christoph Adami, Introduction to Artificial Life
Gérard Weisbuch
 Artif. Life1
1987 Random Iterations of Threshold Networks and Associative Memory
abstract
A threshold network that can memorize and retrieve a given set of patterns $X_s $ is presented. A theoretical framework is given to allow expression of this property in terms of the dynamics of the network; the patterns in $X_s $ must be attractive fixed points. We give sufficient conditions for this, using a concept of energy. Scaling laws for the storing capacity of random patterns are provided and tested in the case of random as well as nonrandom patterns, such as the letters of the alphabet.
Françoise Fogelman-Soulié, Gérard Weisbuch
 SIAM J. Comput.2
1983 Transient length in sequential iteration of threshold functions
Françoise Fogelman-Soulié, Eric Goles Ch., Gérard Weisbuch
 Discret. Appl. Math.3