Search Results for author: Teng Li

Found 16 papers, 7 papers with code

Representation-Agnostic Shape Fields

1 code implementation ICLR 2022 Xiaoyang Huang, Jiancheng Yang, Yanjun Wang, Ziyu Chen, Linguo Li, Teng Li, Bingbing Ni, Wenjun Zhang

In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.

Gradient Correction beyond Gradient Descent

no code implementations16 Mar 2022 Zefan Li, Bingbing Ni, Teng Li, Wenjun Zhang, Wen Gao

GCGD consists of two plug-in modules: 1) inspired by the idea of gradient prediction, we propose a \textbf{GC-W} module for weight gradient correction; 2) based on Neural ODE, we propose a \textbf{GC-ODE} module for hidden states gradient correction.

Contrastive Regression for Domain Adaptation on Gaze Estimation

no code implementations CVPR 2022 Yaoming Wang, Yangzhou Jiang, Jin Li, Bingbing Ni, Wenrui Dai, Chenglin Li, Hongkai Xiong, Teng Li

Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance.

Domain Generalization Gaze Estimation

Shape Self-Correction for Unsupervised Point Cloud Understanding

no code implementations ICCV 2021 Ye Chen, Jinxian Liu, Bingbing Ni, Hang Wang, Jiancheng Yang, Ning Liu, Teng Li, Qi Tian

Then the destroyed shape and the normal shape are sent into a point cloud network to get representations, which are employed to segment points that belong to distorted parts and further reconstruct them to restore the shape to normal.

Self-Supervised Learning

PyTorch Distributed: Experiences on Accelerating Data Parallel Training

2 code implementations28 Jun 2020 Shen Li, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala

This paper presents the design, implementation, and evaluation of the PyTorch distributed data parallel module.

Adversarial Domain Adaptation with Domain Mixup

1 code implementation4 Dec 2019 Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang

In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.

Domain Adaptation

Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search

no code implementations25 Sep 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

As a result, using manually designed action space to perform NAS often leads to sample-inefficient explorations of architectures and thus can be sub-optimal.

Neural Architecture Search

Sample-Efficient Neural Architecture Search by Learning Action Space

1 code implementation17 Jun 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.

Neural Architecture Search

Sample-Efficient Neural Architecture Search by Learning Action Space for Monte Carlo Tree Search

1 code implementation1 Jan 2019 Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

To improve the sample efficiency, this paper proposes Latent Action Neural Architecture Search (LaNAS), which learns actions to recursively partition the search space into good or bad regions that contain networks with similar performance metrics.

Image Classification Neural Architecture Search

MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation

no code implementations19 Oct 2018 Wei Tang, Gui Li, Xinyuan Bao, Teng Li

One generator transforms the conditional person image into a coarse image of the target pose globally, and the other is to enhance the detailed quality of the synthetic person image through a local reinforcement network.

Image Generation

Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning

no code implementations2 Mar 2018 Teng Li, Zhiyuan Xu, Jian Tang, Yanzhi Wang

Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks.

online learning reinforcement-learning

A Roadmap for HEP Software and Computing R&D for the 2020s

1 code implementation18 Dec 2017 Johannes Albrecht, Antonio Augusto Alves Jr, Guilherme Amadio, Giuseppe Andronico, Nguyen Anh-Ky, Laurent Aphecetche, John Apostolakis, Makoto Asai, Luca Atzori, Marian Babik, Giuseppe Bagliesi, Marilena Bandieramonte, Sunanda Banerjee, Martin Barisits, Lothar A. T. Bauerdick, Stefano Belforte, Douglas Benjamin, Catrin Bernius, Wahid Bhimji, Riccardo Maria Bianchi, Ian Bird, Catherine Biscarat, Jakob Blomer, Kenneth Bloom, Tommaso Boccali, Brian Bockelman, Tomasz Bold, Daniele Bonacorsi, Antonio Boveia, Concezio Bozzi, Marko Bracko, David Britton, Andy Buckley, Predrag Buncic, Paolo Calafiura, Simone Campana, Philippe Canal, Luca Canali, Gianpaolo Carlino, Nuno Castro, Marco Cattaneo, Gianluca Cerminara, Javier Cervantes Villanueva, Philip Chang, John Chapman, Gang Chen, Taylor Childers, Peter Clarke, Marco Clemencic, Eric Cogneras, Jeremy Coles, Ian Collier, David Colling, Gloria Corti, Gabriele Cosmo, Davide Costanzo, Ben Couturier, Kyle Cranmer, Jack Cranshaw, Leonardo Cristella, David Crooks, Sabine Crépé-Renaudin, Robert Currie, Sünje Dallmeier-Tiessen, Kaushik De, Michel De Cian, Albert De Roeck, Antonio Delgado Peris, Frédéric Derue, Alessandro Di Girolamo, Salvatore Di Guida, Gancho Dimitrov, Caterina Doglioni, Andrea Dotti, Dirk Duellmann, Laurent Duflot, Dave Dykstra, Katarzyna Dziedziniewicz-Wojcik, Agnieszka Dziurda, Ulrik Egede, Peter Elmer, Johannes Elmsheuser, V. Daniel Elvira, Giulio Eulisse, Steven Farrell, Torben Ferber, Andrej Filipcic, Ian Fisk, Conor Fitzpatrick, José Flix, Andrea Formica, Alessandra Forti, Giovanni Franzoni, James Frost, Stu Fuess, Frank Gaede, Gerardo Ganis, Robert Gardner, Vincent Garonne, Andreas Gellrich, Krzysztof Genser, Simon George, Frank Geurts, Andrei Gheata, Mihaela Gheata, Francesco Giacomini, Stefano Giagu, Manuel Giffels, Douglas Gingrich, Maria Girone, Vladimir V. Gligorov, Ivan Glushkov, Wesley Gohn, Jose Benito Gonzalez Lopez, Isidro González Caballero, Juan R. González Fernández, Giacomo Govi, Claudio Grandi, Hadrien Grasland, Heather Gray, Lucia Grillo, Wen Guan, Oliver Gutsche, Vardan Gyurjyan, Andrew Hanushevsky, Farah Hariri, Thomas Hartmann, John Harvey, Thomas Hauth, Benedikt Hegner, Beate Heinemann, Lukas Heinrich, Andreas Heiss, José M. Hernández, Michael Hildreth, Mark Hodgkinson, Stefan Hoeche, Burt Holzman, Peter Hristov, Xingtao Huang, Vladimir N. Ivanchenko, Todor Ivanov, Jan Iven, Brij Jashal, Bodhitha Jayatilaka, Roger Jones, Michel Jouvin, Soon Yung Jun, Michael Kagan, Charles William Kalderon, Meghan Kane, Edward Karavakis, Daniel S. Katz, Dorian Kcira, Oliver Keeble, Borut Paul Kersevan, Michael Kirby, Alexei Klimentov, Markus Klute, Ilya Komarov, Dmitri Konstantinov, Patrick Koppenburg, Jim Kowalkowski, Luke Kreczko, Thomas Kuhr, Robert Kutschke, Valentin Kuznetsov, Walter Lampl, Eric Lancon, David Lange, Mario Lassnig, Paul Laycock, Charles Leggett, James Letts, Birgit Lewendel, Teng Li, Guilherme Lima, Jacob Linacre, Tomas Linden, Miron Livny, Giuseppe Lo Presti, Sebastian Lopienski, Peter Love, Adam Lyon, Nicolò Magini, Zachary L. Marshall, Edoardo Martelli, Stewart Martin-Haugh, Pere Mato, Kajari Mazumdar, Thomas McCauley, Josh McFayden, Shawn McKee, Andrew McNab, Rashid Mehdiyev, Helge Meinhard, Dario Menasce, Patricia Mendez Lorenzo, Alaettin Serhan Mete, Michele Michelotto, Jovan Mitrevski, Lorenzo Moneta, Ben Morgan, Richard Mount, Edward Moyse, Sean Murray, Armin Nairz, Mark S. Neubauer, Andrew Norman, Sérgio Novaes, Mihaly Novak, Arantza Oyanguren, Nurcan Ozturk, Andres Pacheco Pages, Michela Paganini, Jerome Pansanel, Vincent R. Pascuzzi, Glenn Patrick, Alex Pearce, Ben Pearson, Kevin Pedro, Gabriel Perdue, Antonio Perez-Calero Yzquierdo, Luca Perrozzi, Troels Petersen, Marko Petric, Andreas Petzold, Jónatan Piedra, Leo Piilonen, Danilo Piparo, Jim Pivarski, Witold Pokorski, Francesco Polci, Karolos Potamianos, Fernanda Psihas, Albert Puig Navarro, Günter Quast, Gerhard Raven, Jürgen Reuter, Alberto Ribon, Lorenzo Rinaldi, Martin Ritter, James Robinson, Eduardo Rodrigues, Stefan Roiser, David Rousseau, Gareth Roy, Grigori Rybkine, Andre Sailer, Tai Sakuma, Renato Santana, Andrea Sartirana, Heidi Schellman, Jaroslava Schovancová, Steven Schramm, Markus Schulz, Andrea Sciabà, Sally Seidel, Sezen Sekmen, Cedric Serfon, Horst Severini, Elizabeth Sexton-Kennedy, Michael Seymour, Davide Sgalaberna, Illya Shapoval, Jamie Shiers, Jing-Ge Shiu, Hannah Short, Gian Piero Siroli, Sam Skipsey, Tim Smith, Scott Snyder, Michael D. Sokoloff, Panagiotis Spentzouris, Hartmut Stadie, Giordon Stark, Gordon Stewart, Graeme A. Stewart, Arturo Sánchez, Alberto Sánchez-Hernández, Anyes Taffard, Umberto Tamponi, Jeff Templon, Giacomo Tenaglia, Vakhtang Tsulaia, Christopher Tunnell, Eric Vaandering, Andrea Valassi, Sofia Vallecorsa, Liviu Valsan, Peter Van Gemmeren, Renaud Vernet, Brett Viren, Jean-Roch Vlimant, Christian Voss, Margaret Votava, Carl Vuosalo, Carlos Vázquez Sierra, Romain Wartel, Gordon T. Watts, Torre Wenaus, Sandro Wenzel, Mike Williams, Frank Winklmeier, Christoph Wissing, Frank Wuerthwein, Benjamin Wynne, Zhang Xiaomei, Wei Yang, Efe Yazgan

Particle physics has an ambitious and broad experimental programme for the coming decades.

Computational Physics High Energy Physics - Experiment

Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition

1 code implementation4 Apr 2017 Qichang Hu, Huibing Wang, Teng Li, Chunhua Shen

By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature.

Fine-Grained Image Classification

Peak-Piloted Deep Network for Facial Expression Recognition

no code implementations24 Jul 2016 Xiangyun Zhao, Xiaodan Liang, Luoqi Liu, Teng Li, Yugang Han, Nuno Vasconcelos, Shuicheng Yan

Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently.

Face Recognition Facial Expression Recognition +1

Crowded Scene Analysis: A Survey

no code implementations6 Feb 2015 Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, Shuicheng Yan

Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis.

Anomaly Detection

Beta Process Multiple Kernel Learning

no code implementations CVPR 2014 Bingbing Ni, Teng Li, Pierre Moulin

Specifically, for the kernel representation calculated for each input feature instance, we multiply it element-wise with a latent binary vector named as instance selection variables, which targets at selecting good instances and attenuate the effect of ambiguous ones in the resulting new kernel representation.

Variational Inference

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