Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Schnitzler, François"'
Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR). In this paper, we propose a novel positional encoding method which improves the reconstruction quality of the
Externí odkaz:
http://arxiv.org/abs/2311.06059
End-to-end trainable models have reached the performance of traditional handcrafted compression techniques on videos and images. Since the parameters of these models are learned over large training sets, they are not optimal for any given image to be
Externí odkaz:
http://arxiv.org/abs/2210.04898
Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive most recent advances through deep learning and deep reinforcement learni
Externí odkaz:
http://arxiv.org/abs/2110.06901
Autor:
Suárez-Varela, José, Ferriol-Galmés, Miquel, López, Albert, Almasan, Paul, Bernárdez, Guillermo, Pujol-Perich, David, Rusek, Krzysztof, Bonniot, Loïck, Neumann, Christoph, Schnitzler, François, Taïani, François, Happ, Martin, Maier, Christian, Du, Jia Lei, Herlich, Matthias, Dorfinger, Peter, Hainke, Nick Vincent, Venz, Stefan, Wegener, Johannes, Wissing, Henrike, Wu, Bo, Xiao, Shihan, Barlet-Ros, Pere, Cabellos-Aparicio, Albert
Publikováno v:
ACM SIGCOMM Computer Communication Review, Vol. 51, No. 3, pp. 9-16, 2021
During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This pos
Externí odkaz:
http://arxiv.org/abs/2107.12433
High-level understanding of stories in video such as movies and TV shows from raw data is extremely challenging. Modern video question answering (VideoQA) systems often use additional human-made sources like plot synopses, scripts, video descriptions
Externí odkaz:
http://arxiv.org/abs/2103.14517
Off-policy learning in dynamic decision problems is essential for providing strong evidence that a new policy is better than the one in use. But how can we prove superiority without testing the new policy? To answer this question, we introduce the G-
Externí odkaz:
http://arxiv.org/abs/1502.03255
Autor:
Gal, Avigdor, Mandelbaum, Avishai, Schnitzler, François, Senderovich, Arik, Weidlich, Matthias
Publikováno v:
In Information Systems March 2017 64:266-280
Publikováno v:
Computer Graphics Forum; Feb2022, Vol. 41 Issue 1, p122-157, 36p, 3 Color Photographs, 1 Black and White Photograph, 2 Diagrams, 6 Charts, 2 Graphs
Publikováno v:
Conférence Francophone sur l'Apprentissage Automatique-CAp 2012
Conférence Francophone sur l'Apprentissage Automatique-CAp 2012, Laurent Bougrain, May 2012, Nancy, France. 16 p
Conférence Francophone sur l'Apprentissage Automatique-CAp 2012, Laurent Bougrain, May 2012, Nancy, France. 16 p
National audience; Nous considérons des algorithmes pour apprendre des Mélanges bootstrap d'Arbres de Markov pour l'estimation de densité. Pour les problèmes comportant un grand nombre de variables et peu d'observations, ces mélanges estime
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::515a08a6543ca0de3d70c9dce83313e8
https://hal.inria.fr/hal-00745501/file/cap2012_submission_3.pdf
https://hal.inria.fr/hal-00745501/file/cap2012_submission_3.pdf
Autor:
Piatkowski, Nico, Schnitzler, François
Publikováno v:
Solving Large Scale Learning Tasks. Challenges & Algorithms; 2016, p234-250, 17p