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Patrice Marcotte

dblp:24/4052 · DBLP profile ↗
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6ranked-venue papers
0as first author
0since 2021 · last 2010
0000-0002-1935-2554ORCID · corroborated

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

Computer networks · 3Theory of computation · 2Artificial intelligence and machine learning · 1

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.

Artificial intelligence
1 paper
Deep learning architectures and training · 100%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › feedforward neural network
convex neural network
0.112005
Convex Neural Networks · NIPS 2005
Mathematical optimization › continuous optimization
convex optimization
0.012005
Convex Neural Networks · NIPS 2005

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

convex optimization · 0.1
YearPublicationVenuePosition
2010 A polyhedral study of the network pricing problem with connected toll arcs
abstract
Abstract We consider the problem of setting revenue‐maximizing tolls on a subset of arcs of a transportation network, assuming that the users of the network are assigned to shortest paths with respect to the sum of tolls and initial costs. Our main results are concerned with a polyhedral study of the problem, i.e., the design of valid inequalities and facets for this pricing problem and some of its variants. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010
Géraldine Heilporn, Martine Labbé, Patrice Marcotte, Gilles Savard
Networks3
2007 On a network pricing problem with consecutive toll arcs
Géraldine Heilporn, Martine Labbé, Patrice Marcotte, Gilles Savard
CTW3
2005 Convex Neural Networks
abstract
Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage of many learning algorithms, such as multi-layer artificial neural networks. We show that training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem. This problem involves an infinite number of variables, but can be solved by incrementally inserting a hidden unit at a time, each time finding a linear classifier that minimizes a weighted sum of errors.
Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte
NIPS5
2005 An approximation algorithm for Stackelberg network pricing
abstract
Abstract We consider the problem of maximizing the revenue raised from tolls set on the arcs of a transportation network, under the constraint that users are assigned to toll‐compatible shortest paths. We first prove that this problem is strongly NP‐hard. We then provide a polynomial time algorithm with a worst‐case precision guarantee of ${{1 \over 2}\log_2 m_T+1}$ , where mT denotes the number of toll arcs. Finally, we show that the approximation is tight with respect to a natural relaxation by constructing a family of instances for which the relaxation gap is reached. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 46(1), 57–67 2005
Sébastien Roch, Gilles Savard, Patrice Marcotte
Networks3
1996 A hybrid Tabu-ascent algorithm for the linear Bilevel Programming Problem
Michel Gendreau, Patrice Marcotte, Gilles Savard
J. Glob. Optim.2
1985 On the relationship between Nash - Cournot and Wardrop equilibria
abstract
Abstract A noncooperative game is formulated on a transportation network with congestion. The players are associated with origin‐destination pairs, and are facing demand functions at their respective destination nodes. A Nash‐Cournot equilibrium is defined and conditions for existence and uniqueness of this solution are provided. The asymptotic behavior of the Nash‐Cournot equilibrium is then shown to yield (under appropriate assumptions) a total flow vector corresponding to a Wardrop equilibrium.
Alain Haurie, Patrice Marcotte
Networks2