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Semantic ScholarNathalie Peyrard
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2010 – today
- 2018
[j10]Etienne Auclair, Nathalie Peyrard, Régis Sabbadin:
Réseau bayésien dynamique étiqueté pour l'apprentissage de structure d'un réseau écologique. Revue d'Intelligence Artificielle 32(1): 11-38 (2018)- 2017
- [c10]Etienne Auclair, Nathalie Peyrard, Régis Sabbadin:
Labeled DBN Learning with Community Structure Knowledge. ECML/PKDD (2) 2017: 158-174 - 2015
- [c9]Alexandre Albore, Nathalie Peyrard, Régis Sabbadin, Florent Teichteil-Königsbuch:
An Online Replanning Approach for Crop Fields Mapping with Autonomous UAVs. ICAPS 2015: 259-267 - [c8]Julia Radoszycki, Nathalie Peyrard, Régis Sabbadin:
Solving F3MDPs: Collaborative Multiagent Markov Decision Processes with Factored Transitions, Rewards and Stochastic Policies. PRIMA 2015: 3-19 - [i1]Nathalie Peyrard, Simon de Givry, Alain Franc, Stéphane Robin, Régis Sabbadin, Thomas Schiex, Matthieu Vignes:
Exact and approximate inference in graphical models: variable elimination and beyond. CoRR abs/1506.08544 (2015) - 2014
- [j9]Mathieu Bonneau, Sabrina Gaba, Nathalie Peyrard, Régis Sabbadin:
Reinforcement learning-based design of sampling policies under cost constraints in Markov random fields: Application to weed map reconstruction. Computational Statistics & Data Analysis 72: 30-44 (2014) - [c7]Julia Radoszycki, Nathalie Peyrard, Régis Sabbadin:
Finding good stochastic factored policies for factored Markov decision processes. ECAI 2014: 1083-1084 - 2013
- [j8]M. Charras-Garrido, L. Azizi, Florence Forbes, Senan Doyle, Nathalie Peyrard, D. Abrial:
On the difficulty to delimit disease risk hot spots. Int. J. Applied Earth Observation and Geoinformation 22: 99-105 (2013) - [j7]Gauthier Quesnel, Mahuna Akplogan, Mathieu Bonneau, Roger Martin-Clouaire, Nathalie Peyrard, Jean-Pierre Rellier, Régis Sabbadin, Ronan Trépos:
Décision dans les agro-écosystèmes. Revue d'Intelligence Artificielle 27(4-5): 409-442 (2013) - [j6]Nathalie Peyrard, Régis Sabbadin, Daniel Spring, Barry Brook, Ralph Mac Nally:
Model-based adaptive spatial sampling for occurrence map construction. Statistics and Computing 23(1): 29-42 (2013) - 2012
- [j5]Régis Sabbadin, Nathalie Peyrard, Nicklas Forsell:
A framework and a mean-field algorithm for the local control of spatial processes. Int. J. Approx. Reasoning 53(1): 66-86 (2012) - [c6]Mathieu Bonneau, Nathalie Peyrard, Régis Sabbadin:
A Reinforcement-Learning Algorithm for Sampling Design in Markov Random Fields. ECAI 2012: 181-186 - 2010
- [j4]Alain Franc, M. Goulard, Nathalie Peyrard:
Chordal Graphs to Identify Graphical Model Solutions of Maximum of Entropy Under Constraints on Marginals. SIAM J. Discrete Math. 24(3): 1104-1116 (2010) - [c5]Nathalie Peyrard, Régis Sabbadin, U. Farrokh Niaz:
Decision-theoretic Optimal Sampling in Hidden Markov Random Fields. ECAI 2010: 687-692
2000 – 2009
- 2006
- [c4]Nathalie Peyrard, Régis Sabbadin:
Mean Field Approximation of the Policy Iteration Algorithm for Graph-Based Markov Decision Processes. ECAI 2006: 595-599 - 2005
- [j3]Nathalie Peyrard, Patrick Bouthemy:
Motion-Based Selection of Relevant Video Segments for Video Summarization. Multimedia Tools Appl. 26(3): 259-276 (2005) - 2003
- [j2]Florence Forbes, Nathalie Peyrard:
Hidden Markov Random Field Model Selection Criteria Based on Mean Field-Like Approximations. IEEE Trans. Pattern Anal. Mach. Intell. 25(9): 1089-1101 (2003) - [j1]Gilles Celeux, Florence Forbes, Nathalie Peyrard:
EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognition 36(1): 131-144 (2003) - [c3]Nathalie Peyrard, Patrick Bouthemy:
Detection of meaningful events in videos based on a supervised classification approach. ICIP (3) 2003: 621-624 - [c2]Nathalie Peyrard, Patrick Bouthemy:
Motion-based selection of relevant video segments for video summarisation. ICME 2003: 409-412 - 2002
- [c1]Nathalie Peyrard, Patrick Bouthemy:
Content-Based Video Segmentation using Statistical Motion Models. BMVC 2002: 1-10
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last updated on 2018-03-10 20:06 CET by the dblp team