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Documents  68T05 | enregistrements trouvés : 76

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Research talks;Combinatorics;Computer Science;Probability and Statistics

A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges and can be used to count non-backtracking walks of a given length. It has been used recently in the context of community detection and has appeared previously in connection with the Ihara zeta function and in some generalizations of Ramanujan graphs. In this work, we study the largest eigenvalues of the non-backtracking matrix of the Erdos-Renyi random graph and of the Stochastic Block Model in the regime where the number of edges is proportional to the number of vertices. Our results confirm the "spectral redemption" conjecture that community detection can be made on the basis of the leading eigenvectors above the feasibility threshold. A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges and can be used to count non-backtracking walks of a given length. It has been used recently in the context of community detection and has appeared previously in connection with the Ihara zeta function and in some generalizations of Ramanujan graphs. In this work, we ...

05C50 ; 05C80 ; 68T05 ; 91D30

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Research talks;Computer Science

68Wxx ; 68P05 ; 68M11 ; 68U20 ; 68Q80 ; 68T05 ; 94A60 ; 94A08

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Research talks;Computer Science;Numerical Analysis and Scientific Computing

In this presentation based on on-line demonstrations of algorithms and on the examination of several practical examples, I will reflect on the problem of modeling a detection task in images. I will place myself in the (very frequent) case where the detection task can not be formulated in a Bayesian framework or, rather equivalently that can not be solved by simultaneous learning of the model of the object and that of the background. (In the case where there are plenty of examples of the background and of the object to be detected, the neural networks provide a practical answer, but without explanatory power). Nevertheless for the detection without "learning", I will show that we can not avoid building a background model, or possibly learn it. But this will not require many examples.

Joint works with Axel Davy, Tristan Dagobert, Agnes Desolneux, Thibaud Ehret.
In this presentation based on on-line demonstrations of algorithms and on the examination of several practical examples, I will reflect on the problem of modeling a detection task in images. I will place myself in the (very frequent) case where the detection task can not be formulated in a Bayesian framework or, rather equivalently that can not be solved by simultaneous learning of the model of the object and that of the background. (In the case ...

65D18 ; 68U10 ; 68T05

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- 132 p.

C.I.M. , 0021

Localisation : Colloque 1er étage (COIM)

système complexe # logique floue # réseau neuronal # calcul évolutif # complexité

68T05 ; 93C42

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ISBN 978-0-521-47278-4

Publications of the Newton Institut , 0004

Localisation : Colloque 1er étage (CAMB)

contrôle de comportement guidé visuellement # guidage de robot visuel à partir de stéréo non-calibrée # traquage et contrôle de voitures jouets # traquage visuel # vision et exploration basées sur un modèle # vision informatique en temps réel

68T05 ; 68T10 ; 68Txx ; 68U10

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ISBN 978-0-521-65263-6

Publications of the newton institute

Localisation : Colloque 1er étage (CAMB)

analyse bayesienne # analyse de prototype en ligne # analyse en composante principale # analyse statistique # apprentissage # apprentissage en ligne # approximation stochastique # enseignement en ligne # réseau de neurone

68T05 ; 82-06

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ISBN 978-1-55860-300-4

Localisation : Colloque 1er étage (CHAM)

apprentissage machine # langage naturel # paneau # panel # physique naive # planning # programmation logique # qualitatif # raisonnement # reseau neuronnaux # robotique et vision # vidéo # vision

68T05 ; 68T25 ; 68T27 ; 68T30 ; 68T35 ; 68Txx ; 68Uxx

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ISBN 978-1-55860-300-4

Localisation : Colloque 1er étage (CHAM)

AI distribuée # automate # automatique # intelligence artificielle # modèle co-positif # problème de satisfaction des contraintes # représentation des connaissances # système intelligent de Tutoring # technologie des connaissances de base

68T05 ; 68T20 ; 68T30 ; 68T35 ; 68Txx

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- xiv; 370 p.
ISBN 978-0-8218-4380-2

DIMACS series in discrete mathematics and theoretical computer science , 0069

Localisation : Collection 1er étage

combinatoires # informatique # chimie # graphe

68R10 ; 68T05 ; 68T35 ; 05C35 ; 05C30 ; 05C62 ; 05-06 ; 68-06 ; 00B25

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- Pp. 453-909
ISBN 978-0-904933-61-1

Localisation : Colloque 1er étage (AVIG)

application # contrôle # gestion # industrie des systèmes experts # intelligence artificielle # outil et technique de construction des systèmes experts # surveillance # système expert # système informatique # sécurité # télécommunication # économie des systèmes experts # électrotechnique

68T05 ; 68T35 ; 68Txx ; 68U07 ; 68U20 ; 68U30 ; 68Uxx

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- 449 p.
ISBN 978-0-904933-61-1

Localisation : Colloque 1er étage (AVIG)

application # contrôle # gestion # industrie des systèmes experts # intelligence artificielle # outil et technique de construction des systèmes experts # surveillance # système expert # système informatique # sécurité # télécommunication # économie des systèmes experts # électrotechnique

68T05 ; 68T35 ; 68Txx ; 68U07 ; 68U20 ; 68U30 ; 68Uxx

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ISBN 978-0-8186-0742-4

Localisation : Colloque 1er étage (PARI)

algorithme # application biomédicale # architecture # automate # bord # classification des données # connaissance de base de reconnaissance de forme # inférence et apprentissage # limite # tomagraphie # traitement des images # traitement du signal # vision

68Q20 ; 68Q25 ; 68T05 ; 68T10 ; 68T30 ; 68Txx ; 68U05 ; 68U10 ; 68U30 ; 68Uxx

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ISBN 978-0-8186-0742-4

Localisation : Colloque 1er étage (PARI)

algorithme # application biomédicale # architecture # automate # bord # classification des données # connaissance de base de reconnaissance de forme # inférence et apprentissage # limite # tomographie # traitement des images # traitement du signal # vision

68Q20 ; 68Q25 ; 68T05 ; 68T10 ; 68T30 ; 68Txx ; 68U05 ; 68U10 ; 68U30 ; 68Uxx

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- xiii; 538 p.
ISBN 978-0-941743-64-8

Localisation : Colloque 1er étage (ANTI)

analyse multivariée # analyse factorielle # acquisition des connaissances # système expert # apprentissage automatique # ordinateur neuronal

62-06 ; 62-07 ; 68Q32 ; 68Txx ; 68T05 ; 68T30

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- 224 p.
ISBN 978-0-8218-3483-1

Contemporary mathematics , 0349

Localisation : Collection 1er étage

théorie des groupes # groupe de permutation # groupe non-abélien # théorie quantique # informatique # algorithme # groupe d'automorphisme # calcul quantique

20B40 ; 20E05 ; 20F28 ; 81P68 ; 68Q05 ; 68Q17 ; 68Q42 ; 68Q45 ; 68T05

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- xi; 343 p.
ISBN 978-3-642-24872-6

Lecture notes in artificial intelligence , 6992

Localisation : Colloque 1er étage (PISC)

théorie de la décision # décision multicritère # informatique # programmation

90-06 ; 68-06 ; 91-06 ; 68T05 ; 90B50 ; 90C29 ; 91B06 ; 00B25

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Research talks;Computer Science;Mathematics in Science and Technology

Start the video and click on the track button in the timeline to move to talk 1, 2 and to the discussion.

- Talk 1: Gianluigi Mongillo - Inhibitory connectivity defines the realm of excitatory plasticity

- Talk 2: Menahem Segal - Determinants of network activity: Lessons from dissociated hippocampal lectures

- Discussion with Gianluigi Mongillo and Menahem Segal

92B20 ; 92C20 ; 68T05 ; 68Uxx

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Research talks;Computer Science;Probability and Statistics

Random forests are among the most popular supervised machine learning methods. One of their most practically useful features is the possibility to derive from the ensemble of trees an importance score for each input variable that assesses its relevance for predicting the output. These importance scores have been successfully applied on many problems, notably in bioinformatics, but they are still not well understood from a theoretical point of view. In this talk, I will present our recent works towards a better understanding, and consequently a better exploitation, of these measures. In the first part of my talk, I will present a theoretical analysis of the mean decrease impurity importance in asymptotic ensemble and sample size conditions. Our main results include an explicit formulation of this measure in the case of ensemble of totally randomized trees and a discussion of the conditions under which this measure is consistent with respect to a common definition of variable relevance. The second part of the talk will be devoted to the analysis of finite tree ensembles in a constrained framework that assumes that each tree can be built only from a subset of variables of fixed size. This setting is motivated by very high dimensional problems, or embedded systems, where one can not assume that all variables can fit into memory. We first consider a simple method that grows each tree on a subset of variables randomly and uniformly selected among all variables. We analyse the consistency and convergence rate of this method for the identification of all relevant variables under various problem and algorithm settings. From this analysis, we then motivate and design a modified variable sampling mechanism that is shown to significantly improve convergence in several conditions. Random forests are among the most popular supervised machine learning methods. One of their most practically useful features is the possibility to derive from the ensemble of trees an importance score for each input variable that assesses its relevance for predicting the output. These importance scores have been successfully applied on many problems, notably in bioinformatics, but they are still not well understood from a theoretical point of ...

62H30 ; 68T05

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Research talks;Computer Science;Mathematics in Science and Technology

Start the video and click on the track button in the timeline to move to talk 1, 2 and to the discussion.

- Talk 1: Paul Apicella - Striatal dopamine and acetylcholine mechanisms involved in reward-related learning

The midbrain dopamine system has been identified as a major component of motivation and reward processing. One of its main targets is the striatum which plays an important role in motor control and learning functions. Other subcortical neurons work in parallel with dopamine neurons. In particular, striatal cholinergic interneurons participate in signaling the reward-related significance of stimuli and they may act in concert with dopamine to encode prediction error signals and control the learning of stimulus-response associations. Recent studies have revealed functional cooperativity between these two neuromodulatory systems of a complexity far greater than previously appreciated. In this talk I will review the difference and similarities between dopamine and acetylcholine reward-signaling systems, the possible nature of reward representation in each system, and discuss the involvement of striatal dopamine-acetylcholine interactions during leaning and behavior.

- Talk 2: Yonatan Loewenstein - Modeling operant learning: from synaptic plasticity to behavior

- Discussion with Paul Apicella and Yonatan Loewenstein
Start the video and click on the track button in the timeline to move to talk 1, 2 and to the discussion.

- Talk 1: Paul Apicella - Striatal dopamine and acetylcholine mechanisms involved in reward-related learning

The midbrain dopamine system has been identified as a major component of motivation and reward processing. One of its main targets is the striatum which plays an important role in motor control and learning functions. Other ...

68T05 ; 68Uxx ; 92B20 ; 92C20 ; 92C40

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Research School;Computer Science

In this talk I will present some recent developments in model-free reinforcement learning applied to large state spaces, with an emphasis on deep learning and its role in estimating action-value functions. The talk will cover a variety of model-free algorithms, including variations on Q-Learning, and some of the main techniques that make the approach practical. I will illustrate the usefulness of these methods with examples drawn from the Arcade Learning Environment, the popular set of Atari 2600 benchmark domains. In this talk I will present some recent developments in model-free reinforcement learning applied to large state spaces, with an emphasis on deep learning and its role in estimating action-value functions. The talk will cover a variety of model-free algorithms, including variations on Q-Learning, and some of the main techniques that make the approach practical. I will illustrate the usefulness of these methods with examples drawn from the Arcade ...

68Q32 ; 91A25 ; 68T05

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