Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Mattos, César"'
Autor:
de Souza, Daniel Augusto, Nikitin, Alexander, John, ST, Ross, Magnus, Álvarez, Mauricio A., Deisenroth, Marc Peter, Gomes, João P. P., Mesquita, Diego, Mattos, César Lincoln C.
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the correlation distance of function values. However, selecting an appropr
Externí odkaz:
http://arxiv.org/abs/2310.11527
Autor:
Hämäläinen, Joonas, Hubermont, Antoine, Souza, Amauri, Mattos, César L. C., Gomes, João P. P., Kärkkäinen, Tommi
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the
Externí odkaz:
http://arxiv.org/abs/2305.05518
Autor:
Matias, Alan L.S., Gomes, João Paulo P., Mattos, César Lincoln C., Rocha Neto, Ajalmar R., Mesquita, Diego
Publikováno v:
In Applied Soft Computing September 2024 163
Autor:
Rocha, Atslands, Monteiro, Matheus, Mattos, César, Dias, Madson, Soares, Jorge, Magalhães, Regis, Macedo, José
Publikováno v:
In Computers and Electrical Engineering May 2024 116
Autor:
Dias, Madson L. D., Mattos, César Lincoln C., da Silva, Ticiana L. C., de Macedo, José Antônio F., Silva, Wellington C. P.
The task of detecting anomalous data patterns is as important in practical applications as challenging. In the context of spatial data, recognition of unexpected trajectories brings additional difficulties, such as high dimensionality and varying pat
Externí odkaz:
http://arxiv.org/abs/2004.05958
Autor:
Thompson, Alexander, Danta, Mark, Poursoltan, Pirooz, Kiberu, Andrew, Chittajallu, Renuka, Sood, Siddarth, Stauber, Rudolf, Pinter, Matthias, Peck-Radosavljevic, Markus, Decaestecker, Jochen, Cuyle, Pieter-Jan, Verset, Gontran, Van Vlierberghe, Hans, De Azevedo, Sergio, Andrade, Livia, Cunha Júnior, Ademar, Faria, Luiza, Yen, Cheng Tzu, Colli, Leandro, Asselah, Jamil, Kavan, Petr, Marquez, Vladimir, Brahmania, Mayur, Li, Qiang, Xing, Baocai, Guo, Yabing, Chen, Zhendong, Zhao, Haitao, Peng, Tao, Wang, Liming, Wang, Lu, Liu, Hongming, Wu, Feixiang, Qin, Lunxiu, Zheng, Qichang, Ying, Jieer, Li, Haitao, Wen, Tianfu, Qin, Shukui, Wen, Xiaoyu, Liu, Yunpeng, Chen, Minshan, Wang, Boqing, Bai, Yuxian, He, Yifu, Zhao, Hong, Zhou, Dong, Dai, Chaoliu, Teng, Gaojun, Cui, Shuzhong, Gao, Yi, Zhang, Xizhi, Lu, Zheng, Yin, Tao, Ding, Youming, Jia, Weidong, Xia, Yongxiang, Sun, Beicheng, Xia, Qiang, Yuan, Yufeng, Sun, Huichuan, Shi, Xuetao, Guzmán, Adrián, Corrales, Luis, Kral, Zdenek, Priester, Peter, Kubala, Eugen, Blanc, Jean Frederic, Bourliere, Marc, Peron, Jean Marie, Borg, Christophe, Bronowicki, Jean-Pierre, Ganne, Nathalie, Decaens, Thomas, Uguen, Thomas, Heurgue, Alexandra, Trojan, Joerg, Gonzalez-Carmona, Maria Angeles, Roderburg, Christoph, Ettrich, Thomas, Schotten, Clemens, Kandulski, Arne, Yau, Thomas, Chan, Lam, Scartozzi, Mario, Masi, Gianluca, Fanello, Silvia, Battezzati, Pier Maria, Leonardi, Francesco, Ghidini, Michele, Numata, Kazushi, Morimoto, Manabu, Hidaka, Hisashi, Tsuchiya, Kaoru, Yamashita, Tatsuya, Kato, Naoya, Kudo, Masatoshi, Hagihara, Atsushi, Koga, Hironori, Arakawa, Tomohiro, Nakamura, Ikuo, Kawamura, Yusuke, Kawaoka, Tomokazu, Shimada, Mitsuo, Hasegawa, Kiyoshi, Marusawa, Hiroyuki, Nakamura, Shinchiro, Hiraoka, Atsushi, Hayashi, Hiromitsu, Takeda, Shin, Lee, Han Chu, Paik, Seung Woon, Kim, Do Young, Lee, Jung Il, Jeong, Sook-Hyang, Kim, Won, Tak, Won Young, Heo, Jeong, Kim, Hyeyeong, Chon, Hong Jae, Cheong, Jaeyoun, Yoon, Seung Kew, Yoon, Jung-Hwan, Villalobos, Ricardo, Martinez Rodriguez, Jorge Luis, Oyervides Juarez, Victor, Hernández, Carlos Alberto, Klumpen, Heinz-Josef, de Vos-Geelen, Judith, Gane, Edward, Montenegro, Paola, Torres Mattos, Cesar, Janczewska, Ewa, Kawecki, Maciej, Nowakowska-Zajdel, Ewa, Fedenko, Alexander, Granov, Dmitrii, Alyasova, Anna, Sekacheva, Marina, Ledin, Evgeny, Samol, Jens, Toh, Han Chong, Calvo Campos, Mariona, Gomez Martin, Carlos, Lopez Lopez, Carlos, Muñoz Martin, Andres Jesus, Calleja Panero, Jose Luis, Montero Alvarez, Jose Luis, Reig Monzón, Maria, Delgado Mingorance, Ignacio, Minguez Rosique, Beatriz, Cheng, Ann Lii, Huang, Yi-Hsiang, Lin, Shi-Ming, Huang, Jee-Fu, Yu, Ming-Lung, Su, Wei-Wen, Korphaisarn, Krittiya, Maneenil, Kunlatida, Samdaengpan, Chayanee, Tharavichitkul, Ekkapong, Ozguroglu, Mustafa, Kose, Fatih, Harputluoglu, Hakan, Buchschacher, Gary, Thuluvath, Paul, Xiong, Henry, Patel, Mital, Gold, Philip, Li, Daneng, Brooks, Gabriel, Masood, Ashiq, Patel, Reema, George, Ben, Salgia, Reena, Manji, Gulam, Crow, Mary, Kaseb, Ahmed, Dugan, Matthew, Kadakia, Kunal, Kardosh, Adel, Gibbs, John, Shah, Ashesh, Burris III, Howard, Hsiehchen, David, Cheng, Ann-Lii *, Kaseb, Ahmed O *, Yopp, Adam C *, Zhou, Jian, Nakamura, Shinichiro, Cha, Edward, Hack, Stephen P, Lian, Qinshu, Ma, Ning, Spahn, Jessica H, Wang, Yulei, Wu, Chun, Chow, Pierce K H *, *
Publikováno v:
In The Lancet 18-24 November 2023 402(10415):1835-1847
Autor:
Hämäläinen, Joonas, Alencar, Alisson S. C., Kärkkäinen, Tommi, Mattos, César L. C., Júnior, Amauri H. Souza, Gomes, João P. P.
The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference
Externí odkaz:
http://arxiv.org/abs/1909.09978
Autor:
Vasconcelos, Thiago de P., de Souza, Daniel A. R. M. A., Mattos, César L. C., Gomes, João P. P.
Bayesian Optimization (BO) is a framework for black-box optimization that is especially suitable for expensive cost functions. Among the main parts of a BO algorithm, the acquisition function is of fundamental importance, since it guides the optimiza
Externí odkaz:
http://arxiv.org/abs/1908.00361
Autor:
de Souza, Daniel Augusto R. M. A., Mesquita, Diego, Mattos, César Lincoln C., Gomes, João Paulo P.
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models. Nonetheless, the standard GPLVM variational inference approach
Externí odkaz:
http://arxiv.org/abs/1907.01867
We show theoretical similarities between the Least Squares Support Vector Regression (LS-SVR) model with a Radial Basis Functions (RBF) kernel and maximum a posteriori (MAP) inference on Bayesian RBF networks with a specific Gaussian prior on the reg
Externí odkaz:
http://arxiv.org/abs/1905.00332