no code implementations • 22 May 2024 • Ziqing Wen, Xiaoge Deng, Ping Luo, Tao Sun, Dongsheng Li
Score-based generative models have demonstrated significant practical success in data-generating tasks.
no code implementations • 22 Apr 2024 • Tao Sun, Yuanzi Fu, Kaicheng Yang, Jian Wu, Ziyong Feng
This paper presents the winning solution for the 1st SkatingVerse Challenge.
1 code implementation • 17 Apr 2024 • Xin Li, Kun Yuan, Yajing Pei, Yiting Lu, Ming Sun, Chao Zhou, Zhibo Chen, Radu Timofte, Wei Sun, HaoNing Wu, ZiCheng Zhang, Jun Jia, Zhichao Zhang, Linhan Cao, Qiubo Chen, Xiongkuo Min, Weisi Lin, Guangtao Zhai, Jianhui Sun, Tianyi Wang, Lei LI, Han Kong, Wenxuan Wang, Bing Li, Cheng Luo, Haiqiang Wang, Xiangguang Chen, Wenhui Meng, Xiang Pan, Huiying Shi, Han Zhu, Xiaozhong Xu, Lei Sun, Zhenzhong Chen, Shan Liu, Fangyuan Kong, Haotian Fan, Yifang Xu, Haoran Xu, Mengduo Yang, Jie zhou, Jiaze Li, Shijie Wen, Mai Xu, Da Li, Shunyu Yao, Jiazhi Du, WangMeng Zuo, Zhibo Li, Shuai He, Anlong Ming, Huiyuan Fu, Huadong Ma, Yong Wu, Fie Xue, Guozhi Zhao, Lina Du, Jie Guo, Yu Zhang, huimin zheng, JunHao Chen, Yue Liu, Dulan Zhou, Kele Xu, Qisheng Xu, Tao Sun, Zhixiang Ding, Yuhang Hu
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
2 code implementations • 16 Apr 2024 • Bin Ren, Nancy Mehta, Radu Timofte, Hongyuan Yu, Cheng Wan, Yuxin Hong, Bingnan Han, Zhuoyuan Wu, Yajun Zou, Yuqing Liu, Jizhe Li, Keji He, Chao Fan, Heng Zhang, Xiaolin Zhang, Xuanwu Yin, Kunlong Zuo, Bohao Liao, Peizhe Xia, Long Peng, Zhibo Du, Xin Di, Wangkai Li, Yang Wang, Wei Zhai, Renjing Pei, Jiaming Guo, Songcen Xu, Yang Cao, ZhengJun Zha, Yan Wang, Yi Liu, Qing Wang, Gang Zhang, Liou Zhang, Shijie Zhao, Long Sun, Jinshan Pan, Jiangxin Dong, Jinhui Tang, Xin Liu, Min Yan, Menghan Zhou, Yiqiang Yan, Yixuan Liu, Wensong Chan, Dehua Tang, Dong Zhou, Li Wang, Lu Tian, Barsoum Emad, Bohan Jia, Junbo Qiao, Yunshuai Zhou, Yun Zhang, Wei Li, Shaohui Lin, Shenglong Zhou, Binbin Chen, Jincheng Liao, Suiyi Zhao, Zhao Zhang, Bo wang, Yan Luo, Yanyan Wei, Feng Li, Mingshen Wang, Yawei Li, Jinhan Guan, Dehua Hu, Jiawei Yu, Qisheng Xu, Tao Sun, Long Lan, Kele Xu, Xin Lin, Jingtong Yue, Lehan Yang, Shiyi Du, Lu Qi, Chao Ren, Zeyu Han, YuHan Wang, Chaolin Chen, Haobo Li, Mingjun Zheng, Zhongbao Yang, Lianhong Song, Xingzhuo Yan, Minghan Fu, Jingyi Zhang, Baiang Li, Qi Zhu, Xiaogang Xu, Dan Guo, Chunle Guo, Jiadi Chen, Huanhuan Long, Chunjiang Duanmu, Xiaoyan Lei, Jie Liu, Weilin Jia, Weifeng Cao, Wenlong Zhang, Yanyu Mao, Ruilong Guo, Nihao Zhang, Qian Wang, Manoj Pandey, Maksym Chernozhukov, Giang Le, Shuli Cheng, Hongyuan Wang, Ziyan Wei, Qingting Tang, Liejun Wang, Yongming Li, Yanhui Guo, Hao Xu, Akram Khatami-Rizi, Ahmad Mahmoudi-Aznaveh, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
no code implementations • 9 Apr 2024 • Anxin Yang, Zhijuan Du, Tao Sun
Substitute relationships are fundamental to people's daily lives across various domains.
no code implementations • 25 Mar 2024 • Ping Luo, Xiaoge Deng, Ziqing Wen, Tao Sun, Dongsheng Li
Federated Learning (FL) is a distributed machine learning framework in communication network systems.
no code implementations • 19 Feb 2024 • Congyun Jin, Ming Zhang, Xiaowei Ma, Li Yujiao, Yingbo Wang, Yabo Jia, Yuliang Du, Tao Sun, Haowen Wang, Cong Fan, Jinjie Gu, Chenfei Chi, Xiangguo Lv, Fangzhou Li, Wei Xue, Yiran Huang
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.
no code implementations • 10 Feb 2024 • Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu, Hangyuan Ji, Zixiang Wang, Tao Sun, Long He, Zhoujun Li, Xi Zhu, Chengwei Pan
Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).
no code implementations • 19 Jan 2024 • Haowen Wang, Tao Sun, Kaixiang Ji, Jian Wang, Cong Fan, Jinjie Gu
We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks.
no code implementations • 15 Jan 2024 • Haowen Wang, Yuliang Du, Congyun Jin, Yujiao Li, Yingbo Wang, Tao Sun, Piqi Qin, Cong Fan
In this paper, we proposed GACE, a graph-based cross-page ads embedding generation method.
no code implementations • 13 Jan 2024 • Linzheng Chai, Jian Yang, Tao Sun, Hongcheng Guo, Jiaheng Liu, Bing Wang, Xiannian Liang, Jiaqi Bai, Tongliang Li, Qiyao Peng, Zhoujun Li
To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCOT) to transfer knowledge from high-resource languages to low-resource languages.
no code implementations • 6 Dec 2023 • Haowen Wang, Tao Sun, Cong Fan, Jinjie Gu
Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization.
no code implementations • 15 Nov 2023 • Tao Sun, Yan Hao, Shengyu Huang, Silvio Savarese, Konrad Schindler, Marc Pollefeys, Iro Armeni
To this end, we introduce the Nothing Stands Still (NSS) benchmark, which focuses on the spatiotemporal registration of 3D scenes undergoing large spatial and temporal change, ultimately creating one coherent spatiotemporal map.
no code implementations • 23 Oct 2023 • Tao Sun, Congliang Chen, Peng Qiao, Li Shen, Xinwang Liu, Dongsheng Li
Sign-based stochastic methods have gained attention due to their ability to achieve robust performance despite using only the sign information for parameter updates.
no code implementations • 4 Oct 2023 • Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, Hongxia Yang
Despite policy-based RL methods dominating the literature on RL for program synthesis, the nature of program synthesis tasks hints at a natural alignment with value-based methods.
no code implementations • 2 Oct 2023 • Yunwen Lei, Tao Sun, Mingrui Liu
We show both minibatch and local SGD achieve a linear speedup to attain the optimal risk bounds.
no code implementations • 18 Aug 2023 • Xiaoge Deng, Li Shen, Shengwei Li, Tao Sun, Dongsheng Li, DaCheng Tao
Stochastic gradient descent (SGD) performed in an asynchronous manner plays a crucial role in training large-scale machine learning models.
no code implementations • 17 Aug 2023 • Jiaqi Yang, Yucong Chen, Xiangting Meng, Chenxin Yan, Min Li, Ran Cheng, Lige Liu, Tao Sun, Laurent Kneip
The object pose predictor then generates canonical object representations from RGB images.
no code implementations • 7 Aug 2023 • Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu
Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making.
no code implementations • 23 May 2023 • Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning, Wei Fang, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian
However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.
no code implementations • 6 May 2023 • Taofeng Xie, Chentao Cao, Zhuoxu Cui, Yu Guo, Caiying Wu, Xuemei Wang, Qingneng Li, Zhanli Hu, Tao Sun, Ziru Sang, Yihang Zhou, Yanjie Zhu, Dong Liang, Qiyu Jin, Guoqing Chen, Haifeng Wang
JPD of MRI and noise-added PET was learned in the diffusion process.
no code implementations • 20 Apr 2023 • Tao Sun, Bojian Yin, Sander Bohte
Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware.
1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
1 code implementation • 27 Mar 2023 • Tao Sun, Lu Pang, Chao Chen, Haibin Ling
It detects possible triggers in the token space using image structural similarity and label consistency between the test image and MAE restorations.
no code implementations • 14 Feb 2023 • Ye Yue, Marc Baltes, Nidal Abujahar, Tao Sun, Charles D. Smith, Trevor Bihl, Jundong Liu
Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data.
no code implementations • 29 Jan 2023 • Peng Qiao, Sidun Liu, Tao Sun, Ke Yang, Yong Dou
It provides a promising way to introduce the Transformer in low-level vision tasks.
1 code implementation • CVPR 2023 • Lu Pang, Tao Sun, Haibin Ling, Chao Chen
In experiments, we show that our method, trained without labels, is on-par with state-of-the-art defense methods trained using labels.
no code implementations • 12 Oct 2022 • Tao Sun, Nidal Abuhajar, Shuyu Gong, Zhewei Wang, Charles D. Smith, Xianhui Wang, Li Xu, Jundong Liu
Speaker separation aims to extract multiple voices from a mixed signal.
1 code implementation • ICCV 2023 • Tao Sun, Cheng Lu, Haibin Ling
In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue.
1 code implementation • 26 Aug 2022 • Tao Sun, Cheng Lu, Haibin Ling
We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works.
1 code implementation • 11 Aug 2022 • Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang
Theoretically, we show that the adaptive regularization of \ReTwo{AIR} enhances the implicit regularization and vanishes at the end of training.
1 code implementation • 18 Jul 2022 • Tao Sun, Cheng Lu, Haibin Ling
We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels.
1 code implementation • CVPR 2022 • Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc van Gool, Bernt Schiele, Federico Tombari, Fisher Yu
Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous driving systems.
1 code implementation • 25 May 2022 • Jiaxin Wei, Lige Liu, Ran Cheng, Wenqing Jiang, Minghao Xu, Xinyu Jiang, Tao Sun, Soren Schwertfeger, Laurent Kneip
Recent years have witnessed the surge of learned representations that directly build upon point clouds.
no code implementations • 12 May 2022 • Ran Cheng, Xinyu Jiang, Yuan Chen, Lige Liu, Tao Sun
In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization.
1 code implementation • 28 Apr 2022 • Boqing Zhu, Kele Xu, Changjian Wang, Zheng Qin, Tao Sun, Huaimin Wang, Yuxing Peng
We present an approach to learn voice-face representations from the talking face videos, without any identity labels.
1 code implementation • CVPR 2022 • Tao Sun, Cheng Lu, Tianshuo Zhang, Haibin Ling
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.
1 code implementation • 18 Oct 2021 • Tao Sun, Huaming Ling, Zuoqiang Shi, Dongsheng Li, Bao Wang
In this paper, to eliminate the effort for tuning the momentum-related hyperparameter, we propose a new adaptive momentum inspired by the optimal choice of the heavy ball momentum for quadratic optimization.
2 code implementations • 12 Oct 2021 • Zhemin Li, Tao Sun, Hongxia Wang, Bao Wang
Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training.
no code implementations • 4 Oct 2021 • Gautham Ramajayam, Tao Sun, Chiu C. Tan, Lannan Luo, Haibin Ling
Many critical applications rely on cameras to capture video footage for analytical purposes.
2 code implementations • 1 Jul 2021 • Janis Postels, Mattia Segu, Tao Sun, Luca Sieber, Luc van Gool, Fisher Yu, Federico Tombari
We find that, while DUMs scale to realistic vision tasks and perform well on OOD detection, the practicality of current methods is undermined by poor calibration under distributional shifts.
Out of Distribution (OOD) Detection Semantic Segmentation +1
no code implementations • 23 Apr 2021 • Tao Sun, Dongsheng Li, Bao Wang
In FedAvg, clients keep their data locally for privacy protection; a central parameter server is used to communicate between clients.
no code implementations • 2 Feb 2021 • Tao Sun, Dongsheng Li, Bao Wang
The stability and generalization of stochastic gradient-based methods provide valuable insights into understanding the algorithmic performance of machine learning models.
no code implementations • 30 Jan 2021 • Linbo Qiao, Tao Sun, Hengyue Pan, Dongsheng Li
In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.
no code implementations • 31 Dec 2020 • Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich
However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes.
no code implementations • NeurIPS 2020 • Kaiqing Zhang, Tao Sun, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar
In contrast, we model the problem as a robust Markov game, where the goal of all agents is to find policies such that no agent has the incentive to deviate, i. e., reach some equilibrium point, which is also robust to the possible uncertainty of the MARL model.
no code implementations • 24 Nov 2020 • Yunzhe Tao, Sahika Genc, Jonathan Chung, Tao Sun, Sunil Mallya
Accelerating learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low.
no code implementations • 28 Sep 2020 • Yunzhe Tao, Sahika Genc, Tao Sun, Sunil Mallya
Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown.
1 code implementation • 30 Jul 2020 • Bingyao Huang, Tao Sun, Haibin Ling
Full projector compensation aims to modify a projector input image to compensate for both geometric and photometric disturbance of the projection surface.
no code implementations • 12 Jun 2020 • Mohammad Rasouli, Tao Sun, Ram Rajagopal
We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints.
no code implementations • 20 Feb 2020 • Tao Sun, Han Shen, Tianyi Chen, Dongsheng Li
Typically, the performance of TD(0) and TD($\lambda$) is very sensitive to the choice of stepsizes.
no code implementations • 2 Jan 2020 • Sahika Genc, Sunil Mallya, Sravan Bodapati, Tao Sun, Yunzhe Tao
Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem.
no code implementations • 5 Nov 2019 • Bharathan Balaji, Sunil Mallya, Sahika Genc, Saurabh Gupta, Leo Dirac, Vineet Khare, Gourav Roy, Tao Sun, Yunzhe Tao, Brian Townsend, Eddie Calleja, Sunil Muralidhara, Dhanasekar Karuppasamy
DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems.
no code implementations • NeurIPS 2019 • Tao Sun, Yuejiao Sun, Dongsheng Li, Qing Liao
In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method.
no code implementations • 23 Sep 2019 • Tao Sun, Dongsheng Li
Decentralized stochastic gradient method emerges as a promising solution for solving large-scale machine learning problems.
no code implementations • 22 Aug 2019 • Man Qi, Niv DeMalach, Tao Sun, Hailin Zhang
Thus, we developed an extension of resource competition theory to investigate partial and total preemption (in the latter, the preemptor is unaffected by species with lower preemption rank).
no code implementations • 27 Jul 2019 • Tao Sun, Roberto Barrio, Marcos Rodriguez, Hao Jiang
In image processing, Total Variation (TV) regularization models are commonly used to recover blurred images.
no code implementations • 23 Jul 2019 • Tao Sun, Dongsheng Li, Zhe Quan, Hao Jiang, Shengguo Li, Yong Dou
In this paper, we answer a question: can the nonconvex heavy-ball algorithms with random initialization avoid saddle points?
no code implementations • 17 May 2019 • Mohammad Rasouli, Tao Sun, Camille Pache, Patrick Panciatici, Jean Maeght, Ramesh Johari, Ram Rajagopal
The methodology consists in modelling the problem as a two-stage stochastic optimization between high priority stochastic grid services and low priority cloud storage for stochastic end users.
no code implementations • CVPR 2019 • Tao Sun, Zonglin Di, Pengyu Che, Chun Liu, Yin Wang
Deep learning is revolutionizing the mapping industry.
no code implementations • ICLR 2019 • Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu
In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem.
1 code implementation • 18 Feb 2019 • Zhen Zhang, Dong-Bo Zhang, Tao Sun, Renata Wentzcovitch
We here introduce a Fortran code that computes anharmonic free energy of solids from first-principles based on our phonon quasiparticle approach.
Materials Science
no code implementations • 9 Feb 2019 • Tao Sun, Dongsheng Li, Hao Jiang, Zhe Quan
In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing.
no code implementations • ICLR 2019 • Tao Sun, Zhewei Wang, C. D. Smith, Jundong Liu
We model this procedure as a traceback pipeline and take it as a central piece to build an end-to-end segmentation network.
no code implementations • 27 Nov 2018 • Xiao Wang, Tao Sun, Rui Yang, Chenglong Li, Bin Luo, Jin Tang
In this paper, we propose an efficient quality-aware deep neural network to model the weight of data from each domain using deep reinforcement learning (DRL).
no code implementations • 22 Nov 2018 • Tao Sun, Yuejiao Sun, Yangyang Xu, Wotao Yin
random and cyclic selections are either infeasible or very expensive.
no code implementations • 5 Nov 2018 • Tao Sun, Penghang Yin, Dongsheng Li, Chun Huang, Lei Guan, Hao Jiang
For objective functions satisfying a relaxed strongly convex condition, the linear convergence is established under weaker assumptions on the step size and inertial parameter than made in the existing literature.
no code implementations • NeurIPS 2018 • Tao Sun, Yuejiao Sun, Wotao Yin
This paper studies Markov chain gradient descent, a variant of stochastic gradient descent where the random samples are taken on the trajectory of a Markov chain.
no code implementations • 11 Sep 2018 • Lei Guan, Linbo Qiao, Dongsheng Li, Tao Sun, Keshi Ge, Xicheng Lu
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection.
1 code implementation • NeurIPS 2018 • Tianyi Chen, Georgios B. Giannakis, Tao Sun, Wotao Yin
This paper presents a new class of gradient methods for distributed machine learning that adaptively skip the gradient calculations to learn with reduced communication and computation.
no code implementations • 23 Jan 2018 • Tao Sun, Linbo Qiao, Dongsheng Li
The non-ergodic O(1/k) rate is proved for proximal inertial gradient descent with constant stepzise when the objective function is coercive.
no code implementations • 12 Sep 2017 • Tao Sun, Hao Jiang, Li-Zhi Cheng, Wei Zhu
In fact, a lot of classical inexact nonconvex and nonsmooth algorithms allow these three conditions.
no code implementations • 1 Sep 2017 • Tao Sun, Hao Jiang, Lizhi Cheng, Wei Zhu
The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem.
no code implementations • ICML 2017 • Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
A naive learning algorithm that uses the noisy sufficient statistics “as is” outperforms general-purpose differentially private learning algorithms.
no code implementations • 14 Jun 2017 • Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
We investigate the problem of learning discrete, undirected graphical models in a differentially private way.