no code implementations • 11 Sep 2023 • Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff, Yoni K. Ashar, Tibor Auer, Sylvain Baillet, Shashank Bansal, Leandro Beltrachini, Christian G. Benar, Giacomo Bertazzoli, Suyash Bhogawar, Ross W. Blair, Marta Bortoletto, Mathieu Boudreau, Teon L. Brooks, Vince D. Calhoun, Filippo Maria Castelli, Patricia Clement, Alexander L Cohen, Julien Cohen-Adad, Sasha D'Ambrosio, Gilles de Hollander, María de la iglesia-Vayá, Alejandro de la Vega, Arnaud Delorme, Orrin Devinsky, Dejan Draschkow, Eugene Paul Duff, Elizabeth Dupre, Eric Earl, Oscar Esteban, Franklin W. Feingold, Guillaume Flandin, anthony galassi, Giuseppe Gallitto, Melanie Ganz, Rémi Gau, James Gholam, Satrajit S. Ghosh, Alessio Giacomel, Ashley G Gillman, Padraig Gleeson, Alexandre Gramfort, Samuel Guay, Giacomo Guidali, Yaroslav O. Halchenko, Daniel A. Handwerker, Nell Hardcastle, Peer Herholz, Dora Hermes, Christopher J. Honey, Robert B. Innis, Horea-Ioan Ioanas, Andrew Jahn, Agah Karakuzu, David B. Keator, Gregory Kiar, Balint Kincses, Angela R. Laird, Jonathan C. Lau, Alberto Lazari, Jon Haitz Legarreta, Adam Li, Xiangrui Li, Bradley C. Love, Hanzhang Lu, Camille Maumet, Giacomo Mazzamuto, Steven L. Meisler, Mark Mikkelsen, Henk Mutsaerts, Thomas E. Nichols, Aki Nikolaidis, Gustav Nilsonne, Guiomar Niso, Martin Norgaard, Thomas W Okell, Robert Oostenveld, Eduard Ort, Patrick J. Park, Mateusz Pawlik, Cyril R. Pernet, Franco Pestilli, Jan Petr, Christophe Phillips, Jean-Baptiste Poline, Luca Pollonini, Pradeep Reddy Raamana, Petra Ritter, Gaia Rizzo, Kay A. Robbins, Alexander P. Rockhill, Christine Rogers, Ariel Rokem, Chris Rorden, Alexandre Routier, Jose Manuel Saborit-Torres, Taylor Salo, Michael Schirner, Robert E. Smith, Tamas Spisak, Julia Sprenger, Nicole C. Swann, Martin Szinte, Sylvain Takerkart, Bertrand Thirion, Adam G. Thomas, Sajjad Torabian, Gael Varoquaux, Bradley Voytek, Julius Welzel, Martin Wilson, Tal Yarkoni, Krzysztof J. Gorgolewski
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
no code implementations • 23 Nov 2022 • Xin Li, Xiangrui Li, Deng Pan, Yao Qiang, Dongxiao Zhu
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i. e., last hidden layer) and a linear classifier (i. e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e. g., cross-entropy).
1 code implementation • 14 Dec 2020 • Xin Li, Xiangrui Li, Deng Pan, Dongxiao Zhu
This inspires us to propose a new Probabilistically Compact (PC) loss with logit constraints which can be used as a drop-in replacement for cross-entropy (CE) loss to improve CNN's adversarial robustness.
no code implementations • 15 Oct 2020 • Yuan Zhou, Xiangrui Li
Then two set of pseudo labels are used to jointly train a student network with the same structure as the teacher.
no code implementations • 12 Jul 2020 • Deng Pan, Xiangrui Li, Xin Li, Dongxiao Zhu
Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items.
1 code implementation • 4 Mar 2020 • Xiangrui Li, Xin Li, Deng Pan, Dongxiao Zhu
Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision.
no code implementations • 23 Feb 2020 • Xiangrui Li, Deng Pan, Xin Li, Dongxiao Zhu
In each iteration of SGD, a mini-batch from the training data is sampled and the true gradient of the loss function is estimated as the noisy gradient calculated on this mini-batch.
no code implementations • 10 Jun 2019 • Xiangrui Li, Dongxiao Zhu
Classification on imbalanced datasets is a challenging task in real-world applications.
no code implementations • 9 Jun 2019 • Xiangrui Li, Jasmine Hect, Moriah Thomason, Dongxiao Zhu
The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups.
no code implementations • 26 May 2019 • Huafeng Liu, Yazhou Yao, Zeren Sun, Xiangrui Li, Ke Jia, Zhenmin Tang
Robust road segmentation is a key challenge in self-driving research.
no code implementations • 26 May 2019 • Huafeng Liu, Xiaofeng Han, Xiangrui Li, Yazhou Yao, Pu Huang, Zhenming Tang
We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network.
no code implementations • 20 Apr 2017 • Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li, Kai Yang, Phillip Levy
In this paper, we propose a new deep feature selection method based on deep architecture.