Zobrazeno 1 - 10
of 7 927
pro vyhledávání: '"Oh In-Hwan"'
Speech Emotion Recognition (SER) analyzes human emotions expressed through speech. Self-supervised learning (SSL) offers a promising approach to SER by learning meaningful representations from a large amount of unlabeled audio data. However, existing
Externí odkaz:
http://arxiv.org/abs/2410.12416
Autor:
Kim, Jung-hun, Oh, Min-hwan
In this study, we consider multi-class multi-server asymmetric queueing systems consisting of $N$ queues on one side and $K$ servers on the other side, where jobs randomly arrive in queues at each time. The service rate of each job-server assignment
Externí odkaz:
http://arxiv.org/abs/2410.10098
Autor:
Kim, Sang Min, Kim, Byeongchan, Sehanobish, Arijit, Choromanski, Krzysztof, Shim, Dongseok, Dubey, Avinava, Oh, Min-hwan
Improving the efficiency and performance of implicit neural representations in 3D, particularly Neural Radiance Fields (NeRF) and Signed Distance Fields (SDF) is crucial for enabling their use in real-time applications. These models, while capable of
Externí odkaz:
http://arxiv.org/abs/2410.09771
Autor:
Nam, Jaehyun, Song, Woomin, Park, Seong Hyeon, Tack, Jihoon, Yun, Sukmin, Kim, Jaehyung, Oh, Kyu Hwan, Shin, Jinwoo
Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the scarcity of labeled data, transfer learning is a conventional approac
Externí odkaz:
http://arxiv.org/abs/2408.11063
We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has sparse structure. In the existing Lasso bandit literature, the compatibi
Externí odkaz:
http://arxiv.org/abs/2406.00823
We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability kernel of the Markov decision processes (MDPs) is parametrized by an unknown transition core with features of state and
Externí odkaz:
http://arxiv.org/abs/2405.20165
Autor:
Lee, Joongkyu, Oh, Min-hwan
In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an assortment based on contextual information, and user feedback follows an MNL choice model. There has been a significant di
Externí odkaz:
http://arxiv.org/abs/2405.09831
Autor:
José M Castellano, Marek Majdan, In-Hwan Oh, John Kelleher, Saša Missoni, Natalija Novokmet, Ella Arensman, João Silva, Guy Fagherazzi, Marianne Van Der Sande, Silvia Riva, Joan B Soriano, David M. Pereira, Julio Ancochea, Michel Vaillant, Jörn Klein, Brecht Devleesschauwer, Patrick Soentjens, Ernestina Menasalvas, Justo Menéndez, Antigona Carmen Trofor, Olga Sánchez-Pernaute, Mihaela Lupse, Dora Buonfrate, Zubair Kabir, Jose L Penalvo, Miguel Górgolas, Paula Villares, Lucia Maria Lotrean, Lucía Llanos, Costas Tsiamis, Seval Akgün, Tamara Ursini, José L. Peñalvo, Elly Mertens, Diana Sagastume, Jelena Dimnjaković, Marija Švajda, Tamara Poljičanin, Jelena Sarac, Enisa Ademović, Ana Lúcia Baltazar, Miran Čoklo, Paula Andrea Diaz Valencia, João C. Fernandes, Enrique Javier Gómez, Paul Hynds, Polychronis Kostoulas, Lucía Llanos Jiménez, Paul Nguewa, Georgie O’Sullivan, Miguel Reina Ortiz, Gloria Soriano, Joan B. Soriano, Fernando Spilki, Mary Elizabeth Tamang, Sabrina Van Ierssel, Jakov Vuković, José M. Castellano, James Cottam, Hanne Van Tiggelen, José Barberán, Mercedes Villareal, Nerea Ruiz del Árbol, Alberto Estirado, Alberto Blázquez Herranz, David Fernandez Lobón, Paloma Chausa, David M Pereira, Morteza Hosseini, Elizabeth Hunter, Brendan Palmer, Milena Man, Mira Florea, Andrei Tudor Cernomaz, Radu Adrian Crisan-Dabija, Cristina Grigorescu, João C Fernandes, Daria Rabarova, Adriana Krsakova, Jaroslava Brnova, Janka Prnova, Jaroslav Slany, Dominika Plancikova, Nisa Boukichou Abdelkader, Adrián Peláez, Elena Ávalos, Gorane Iturricastillo, Arnoldo Santos Oviedo, Sergio Luis Lima, Antonio Herrero, Pablo Minguez, Olympia Lioupi, Eleftherios Meletis, Konstantinos Pateras, Mustafa Asfari, Nicoletta De Santis, Petronille Bogaert, Koen Blot, Miriam Saso, Mathil Vandromme, Ivan Pristas, Marko Brkic, Luka Bočkor, Ivan Dolanc, Antonija Jonjić, Iva Šunić, Tugba Gürgen Erdogan, Süleyman Çetinkünar, Cenk Belibağlı, Kübra Demir, Mustafa Görür, Turgut Bulut, K R NayarSilvia Riva, Carlo Giordani, Petra Golin, Oh In-Hwan, Seok Jun Yoon, Lina Ruíz, Juan Pablo Pérez Bedoya, Oscar Ignacio Mendoza, Camilo Hincapie, Boris Rodriguez, Noël Barengo, Juliane Deise Fleck, Matheus Nunes Weber, Lejla Burnazović-Ristić, Semra Čavaljuga, Džan Ahmed Jesenković, Lejla Džananović
Publikováno v:
BMJ Open, Vol 11, Iss 11 (2021)
Introduction unCoVer—Unravelling data for rapid evidence-based response to COVID-19—is a Horizon 2020-funded network of 29 partners from 18 countries capable of collecting and using real-world data (RWD) derived from the response and provision of
Externí odkaz:
https://doaj.org/article/c9e54ac90148496b8a6f0e5f43c36235
Autor:
Borgioli, Leonardo, Oh, Ki-Hwan, Valle, Valentina, Ducas, Alvaro, Halloum, Mohammad, Medina, Diego Federico Mendoza, Sharifi, Arman, L'opez, Paula A, Cassiani, Jessica, Zefran, Milos, Chen, Liaohai, Giulianotti, Pier Cristoforo
Robotic surgery has reached a high level of maturity and has become an integral part of standard surgical care. However, existing surgeon consoles are bulky, take up valuable space in the operating room, make surgical team coordination challenging, a
Externí odkaz:
http://arxiv.org/abs/2403.13941
This paper studies the optimality of the Follow-the-Perturbed-Leader (FTPL) policy in both adversarial and stochastic $K$-armed bandits. Despite the widespread use of the Follow-the-Regularized-Leader (FTRL) framework with various choices of regulari
Externí odkaz:
http://arxiv.org/abs/2403.05134