Zobrazeno 1 - 10
of 10 456
pro vyhledávání: '"Escalera"'
Autor:
Vidal, Àlex Pujol, Johansen, Anders S., Jahromi, Mohammad N. S., Escalera, Sergio, Nasrollahi, Kamal, Moeslund, Thomas B.
We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory compliance. With the increasing need to adhere to privacy legislation
Externí odkaz:
http://arxiv.org/abs/2411.13332
Image inpainting, the process of restoring missing or corrupted regions of an image by reconstructing pixel information, has recently seen considerable advancements through deep learning-based approaches. In this paper, we introduce a novel deep lear
Externí odkaz:
http://arxiv.org/abs/2411.05705
Autor:
Ballester, Rubén, Röell, Ernst, Schmid, Daniel Bin, Alain, Mathieu, Escalera, Sergio, Casacuberta, Carles, Rieck, Bastian
The rising interest in leveraging higher-order interactions present in complex systems has led to a surge in more expressive models exploiting high-order structures in the data, especially in topological deep learning (TDL), which designs neural netw
Externí odkaz:
http://arxiv.org/abs/2410.02392
We present Agglomerative Token Clustering (ATC), a novel token merging method that consistently outperforms previous token merging and pruning methods across image classification, image synthesis, and object detection & segmentation tasks. ATC merges
Externí odkaz:
http://arxiv.org/abs/2409.11923
Autor:
Cioppa, Anthony, Giancola, Silvio, Somers, Vladimir, Joos, Victor, Magera, Floriane, Held, Jan, Ghasemzadeh, Seyed Abolfazl, Zhou, Xin, Seweryn, Karolina, Kowalczyk, Mateusz, Mróz, Zuzanna, Łukasik, Szymon, Hałoń, Michał, Mkhallati, Hassan, Deliège, Adrien, Hinojosa, Carlos, Sanchez, Karen, Mansourian, Amir M., Miralles, Pierre, Barnich, Olivier, De Vleeschouwer, Christophe, Alahi, Alexandre, Ghanem, Bernard, Van Droogenbroeck, Marc, Gorski, Adam, Clapés, Albert, Boiarov, Andrei, Afanasiev, Anton, Xarles, Artur, Scott, Atom, Lim, ByoungKwon, Yeung, Calvin, Gonzalez, Cristian, Rüfenacht, Dominic, Pacilio, Enzo, Deuser, Fabian, Altawijri, Faisal Sami, Cachón, Francisco, Kim, HanKyul, Wang, Haobo, Choe, Hyeonmin, Kim, Hyunwoo J, Kim, Il-Min, Kang, Jae-Mo, Tursunboev, Jamshid, Yang, Jian, Hong, Jihwan, Lee, Jimin, Zhang, Jing, Lee, Junseok, Zhang, Kexin, Habel, Konrad, Jiao, Licheng, Li, Linyi, Gutiérrez-Pérez, Marc, Ortega, Marcelo, Li, Menglong, Lopatto, Milosz, Kasatkin, Nikita, Nemtsev, Nikolay, Oswald, Norbert, Udin, Oleg, Kononov, Pavel, Geng, Pei, Alotaibi, Saad Ghazai, Kim, Sehyung, Ulasen, Sergei, Escalera, Sergio, Zhang, Shanshan, Yang, Shuyuan, Moon, Sunghwan, Moeslund, Thomas B., Shandyba, Vasyl, Golovkin, Vladimir, Dai, Wei, Chung, WonTaek, Liu, Xinyu, Zhu, Yongqiang, Kim, Youngseo, Li, Yuan, Yang, Yuting, Xiao, Yuxuan, Cheng, Zehua, Li, Zhihao
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field unde
Externí odkaz:
http://arxiv.org/abs/2409.10587
Autor:
Triantafillou, Eleni, Kairouz, Peter, Pedregosa, Fabian, Hayes, Jamie, Kurmanji, Meghdad, Zhao, Kairan, Dumoulin, Vincent, Junior, Julio Jacques, Mitliagkas, Ioannis, Wan, Jun, Hosoya, Lisheng Sun, Escalera, Sergio, Dziugaite, Gintare Karolina, Triantafillou, Peter, Guyon, Isabelle
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearl
Externí odkaz:
http://arxiv.org/abs/2406.09073
Autor:
Ballester, Rubén, Hernández-García, Pablo, Papillon, Mathilde, Battiloro, Claudio, Miolane, Nina, Birdal, Tolga, Casacuberta, Carles, Escalera, Sergio, Hajij, Mustafa
Topological Deep Learning seeks to enhance the predictive performance of neural network models by harnessing topological structures in input data. Topological neural networks operate on spaces such as cell complexes and hypergraphs, that can be seen
Externí odkaz:
http://arxiv.org/abs/2405.14094
Neural Architecture Search (NAS) methods have shown to output networks that largely outperform human-designed networks. However, conventional NAS methods have mostly tackled the single dataset scenario, incuring in a large computational cost as the p
Externí odkaz:
http://arxiv.org/abs/2405.06994
Autor:
Egele, Romain, Junior, Julio C. S. Jacques, van Rijn, Jan N., Guyon, Isabelle, Baró, Xavier, Clapés, Albert, Balaprakash, Prasanna, Escalera, Sergio, Moeslund, Thomas, Wan, Jun
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's
Externí odkaz:
http://arxiv.org/abs/2404.09703
Autor:
Ponce, Pablo Ruiz, Barquero, German, Palmero, Cristina, Escalera, Sergio, Garcia-Rodriguez, Jose
Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great difficulty in modeling the hig
Externí odkaz:
http://arxiv.org/abs/2404.09988