no code implementations • 25 May 2022 • Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
1 code implementation • 3 Feb 2022 • Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.
no code implementations • 13 Dec 2021 • Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen, Xin Tong, Baining Guo
The VirtualCube system is a 3D video conference system that attempts to overcome some limitations of conventional technologies.
1 code implementation • 11 Nov 2021 • Wen-Bo Xie, Zhen Liu, Jaideep Srivastava
One of the main challenges for hierarchical clustering is how to appropriately identify the representative points in the lower level of the cluster tree, which are going to be utilized as the roots in the higher level of the cluster tree for further aggregation.
no code implementations • NeurIPS 2021 • Weiyang Liu, Zhen Liu, Hanchen Wang, Liam Paull, Bernhard Schölkopf, Adrian Weller
In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner.
no code implementations • 9 Oct 2021 • Xin Huang, Xuejiao Tang, Wenbin Zhang, Shichao Pei, Ji Zhang, Mingli Zhang, Zhen Liu, Ruijun Chen, Yiyi Huang
The proposed disease diagnosis system also uses a graphical user interface (GUI) to facilitate users to interact with the expert system.
no code implementations • 29 Sep 2021 • Yuchen Lu, Zhen Liu, Alessandro Sordoni, Aristide Baratin, Romain Laroche, Aaron Courville
In this work, we argue that representations induced by self-supervised learning (SSL) methods should both be expressive and learnable.
no code implementations • 4 Jul 2021 • Xuejiao Tang, Xin Huang, Wenbin Zhang, Travers B. Child, Qiong Hu, Zhen Liu, Ji Zhang
Moreover, the proposed model provides intuitive interpretation into visual commonsense reasoning.
1 code implementation • NeurIPS 2021 • Mingde Zhao, Zhen Liu, Sitao Luan, Shuyuan Zhang, Doina Precup, Yoshua Bengio
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning.
Model-based Reinforcement Learning
Out-of-Distribution Generalization
+1
2 code implementations • 22 May 2021 • Zhen Liu, Wenjie Lin, Xinpeng Li, Qing Rao, Ting Jiang, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
no code implementations • 27 Mar 2021 • Xin Huang, Wenbin Zhang, Xuejiao Tang, Mingli Zhang, Jayachander Surbiryala, Vasileios Iosifidis, Zhen Liu, Ji Zhang
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information.
1 code implementation • 2 Mar 2021 • Weiyang Liu, Rongmei Lin, Zhen Liu, Li Xiong, Bernhard Schölkopf, Adrian Weller
Due to the over-parameterization nature, neural networks are a powerful tool for nonlinear function approximation.
no code implementations • 23 Feb 2021 • Zhen Liu, Xiaoqian Sun, Yu-Bo Wang
This paper extends Bayesian mortality projection models for multiple populations considering the stochastic structure and the effect of spatial autocorrelation among the observations.
Methodology
no code implementations • 6 Dec 2020 • Xuejiao Tang, Liuhua Zhang, Wenbin Zhang, Xin Huang, Vasileios Iosifidis, Zhen Liu, Mingli Zhang, Enza Messina, Ji Zhang
Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate.
no code implementations • 6 Dec 2020 • Xuejiao Tang, Jiong Qiu, Ruijun Chen, Wenbin Zhang, Vasileios Iosifidis, Zhen Liu, Wei Meng, Mingli Zhang, Ji Zhang
An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as well as the number of accidents and relevant damages are minimized.
1 code implementation • CVPR 2021 • Weiyang Liu, Rongmei Lin, Zhen Liu, James M. Rehg, Liam Paull, Li Xiong, Le Song, Adrian Weller
The inductive bias of a neural network is largely determined by the architecture and the training algorithm.
no code implementations • 17 Mar 2020 • Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum.
High Energy Physics - Phenomenology High Energy Physics - Experiment
no code implementations • 15 Jan 2020 • Zhen Liu, Hu Li, Chao Wang
Tie strength prediction, sometimes named weight prediction, is vital in exploring the diversity of connectivity pattern emerged in networks.
no code implementations • 23 Dec 2019 • Xing Liu, Xiao-Jun Wu, Zhen Liu, He-Feng Yin
The technology of face recognition has made some progress in recent years.
no code implementations • 6 Dec 2019 • Jing Ge, Guangyu Gao, Zhen Liu
In order to evaluate the effectiveness and feasibility of the proposed approach, we conduct extensive experiments on typical person search datasdet: CUHK-PEDES, in which our approach achieves the top1 score of 55. 32% as a new state-of-the-art.
1 code implementation • NeurIPS 2019 • Weiyang Liu, Zhen Liu, James M. Rehg, Le Song
By generalizing inner product with a bilinear matrix, we propose the neural similarity which serves as a learnable parametric similarity measure for CNNs.
no code implementations • 23 Oct 2019 • Zhen Liu, Borui Xiao, Yuemeng Li, Yong Fan
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images.
1 code implementation • CVPR 2020 • Rongmei Lin, Weiyang Liu, Zhen Liu, Chen Feng, Zhiding Yu, James M. Rehg, Li Xiong, Le Song
Inspired by the Thomson problem in physics where the distribution of multiple propelling electrons on a unit sphere can be modeled via minimizing some potential energy, hyperspherical energy minimization has demonstrated its potential in regularizing neural networks and improving their generalization power.
1 code implementation • NeurIPS 2019 • Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural networks.
1 code implementation • NeurIPS 2018 • Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.
4 code implementations • NeurIPS 2018 • Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
1 code implementation • CVPR 2018 • Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James M. Rehg, Le Song
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.
no code implementations • ICML 2018 • Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.
no code implementations • 31 Oct 2017 • Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics.
no code implementations • ICML 2018 • Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song
We propose an active teacher model that can actively query the learner (i. e., make the learner take exams) for estimating the learner's status and provably guide the learner to achieve faster convergence.
6 code implementations • 11 Sep 2017 • Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots
Low-shot learning methods for image classification support learning from sparse data.
no code implementations • 15 Sep 2015 • Zhen Liu
With the impressive capability to capture visual content, deep convolutional neural networks (CNN) have demon- strated promising performance in various vision-based ap- plications, such as classification, recognition, and objec- t detection.
1 code implementation • 16 Oct 2009 • Paul Fearnhead, Zhen Liu
We consider Bayesian analysis of a class of multiple changepoint models.