Filtrer vos résultats
- 23
- 9
- 1
- 25
- 3
- 2
- 1
- 1
- 1
- 2
- 30
- 11
- 1
- 1
- 4
- 4
- 8
- 3
- 5
- 2
- 1
- 2
- 1
- 1
- 28
- 5
- 23
- 13
- 13
- 13
- 9
- 8
- 4
- 4
- 4
- 3
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 33
- 22
- 16
- 8
- 6
- 5
- 4
- 4
- 3
- 3
- 3
- 3
- 3
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
33 résultats
|
|
triés par
|
|
Une analyse PAC-Bayésienne de l'adaptation de domaine et sa spécialisation aux classifieurs linéairesConférence sur l'apprentissage automatique, Jul 2013, Villeneuve d'Ascq, France. pp.3
Communication dans un congrès
hal-00850242v1
|
||
|
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian contextNIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montréal, Canada
Communication dans un congrès
hal-01093565v2
|
||
Interpreting Neural Networks as Majority Votes through the PAC-Bayesian TheoryWorkshop on Machine Learning with guarantees @ NeurIPS 2019, Dec 2019, Vancouver, Canada
Communication dans un congrès
hal-02335762v1
|
|||
|
A New PAC-Bayesian Perspective on Domain Adaptation33rd International Conference on Machine Learning (ICML 2016), Jun 2016, New York, NY, United States
Communication dans un congrès
hal-01307045v1
|
||
|
PAC-Bayesian Bounds based on the Rényi DivergenceInternational Conference on Artificial Intelligence and Statistics (AISTATS 2016), May 2016, Cadiz, Spain
Communication dans un congrès
hal-01384783v1
|
||
|
Domain-Adversarial Training of Neural NetworksGabriela Csurka. Domain Adaptation in Computer Vision Applications, Springer, 2017, Advances in Computer Vision and Pattern Recognition, 978-3-319-58346-4. ⟨10.1007/978-3-319-58347-1⟩
Chapitre d'ouvrage
hal-01624607v1
|
||
Dérandomisation des Bornes PAC-BayésiennesCAp 2021, Jun 2021, St Etienne, France
Communication dans un congrès
hal-03328677v1
|
|||
|
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural NetworksNeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada
Communication dans un congrès
hal-02139432v2
|
||
Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêtsCAp 2019 - Conférence sur l'Apprentissage automatique, Jul 2019, Toulouse, France
Communication dans un congrès
hal-02148600v1
|
|||
|
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear ClassifiersInternational Conference on Machine Learning 2013, Jun 2013, Atlanta, United States. pp.738-746
Communication dans un congrès
hal-00822685v1
|
||
Apprentissage de Vote de Majorité par Minimisation d'une C-Borne PAC-BayésienneCAp 2021, Jun 2021, St Etienne, France
Communication dans un congrès
hal-03328689v1
|
|||
|
A New PAC-Bayesian View of Domain AdaptationNIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives, Dec 2015, Montréal, Canada
Communication dans un congrès
hal-01223164v1
|
||
|
Pseudo-Bayesian Learning with Kernel Fourier Transform as PriorThe 22nd International Conference on Artificial Intelligence and Statistics, Apr 2019, Naha, Japan
Communication dans un congrès
hal-01908555v2
|
||
|
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural NetworksML with guarantees -- NeurIPS 2019 workshop, Dec 2019, Vancouver, Canada
Poster de conférence
hal-02482354v1
|
||
|
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization BoundNeurIPS, 2021, Online, France
Communication dans un congrès
hal-03278470v1
|
||
|
PAC-Bayes and Domain AdaptationNeurocomputing, 2020, 379, pp.379-397. ⟨10.1016/j.neucom.2019.10.105⟩
Article dans une revue
hal-01563152v3
|
||
|
Landmark-based Ensemble Learning with Random Fourier Features and Gradient BoostingEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium
Communication dans un congrès
hal-02900044v1
|
||
Théorèmes PAC-Bayésiens pour l'apprentissage multi-vuesConférence Francophone sur l'Apprentissage Automatique (CAp), Jul 2016, Marseille, France
Communication dans un congrès
hal-01329763v1
|
|||
|
PAC-Bayesian Learning and Domain AdaptationMulti-Trade-offs in Machine Learning, NIPS 2012 Workshop, Dec 2012, Lake Tahoe, United States
Communication dans un congrès
hal-00749366v1
|
||
|
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundECML PKDD 2021, Sep 2021, Bilbao, Spain
Communication dans un congrès
hal-03208948v2
|
||
|
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers[Research Report] Université Jean Monnet, Saint-Étienne (42); Département d'Informatique et de Génie Logiciel, Université Laval (Québec); ENS Paris; IST Austria. 2016
Rapport
hal-01134246v3
|
||
|
PAC-Bayesian Contrastive Unsupervised Representation LearningUAI 2020 - Conference on Uncertainty in Artificial Intelligence, Aug 2020, Toronto, Canada
Communication dans un congrès
hal-02401282v1
|
||
|
Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models36th AAAI Conférence on Artificial Intelligence, Feb 2022, Vancouver, Canada
Communication dans un congrès
hal-03505639v1
|
||
|
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting2019
Pré-publication, Document de travail
hal-02148618v1
|
||
|
PAC-Bayesian Theory Meets Bayesian InferenceNeural Information Processing Systems (NIPS 2016), Dec 2016, Barcelone, Spain. pp.1876-1884
Communication dans un congrès
hal-01324072v3
|
||
Une borne PAC-Bayésienne en espérance et son extension à l'apprentissage multivuesConférence Francophone sur l'Apprentissage Automatique (CAp), Jun 2017, Grenoble, France
Communication dans un congrès
hal-01529219v1
|
|||
PAC-Bayesian Analysis for a two-step Hierarchical Mutliview Learning Approach27th European Conference on Machine Learning , Jul 2017, Skpoje, Macedonia
Communication dans un congrès
hal-01769773v1
|
|||
|
A New PAC-Bayesian Perspective on Domain Adaptation[Research Report] Univ Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, F-42023 Saint-Etienne, France; Département d'informatique et de génie logiciel, Université Laval (Québec); INRIA - Sierra Project-Team, Ecole Normale Sup´erieure, Paris, France. 2015
Rapport
hal-01163722v3
|
||
|
A General Framework for the Practical Disintegration of PAC-Bayesian BoundsMachine Learning, In press
Article dans une revue
hal-03143025v3
|
||
PAC-Bayes Bounds for the Risk of the Majority VoteAdvances in Neural Information Processing Systems (NIPS'06), Dec 2006, Vancouver, Canada. pp.769-776
Communication dans un congrès
hal-01352012v1
|
- 1
- 2