Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN2.9k papers
- Centre National de la Recherche ScientifiqueFR2.7k papers
- Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di FrascatiIT2.6k papers
- Institute of High Energy PhysicsCN2.6k papers
- University of Science and Technology of ChinaCN2.5k papers
- Peking UniversityCN2.4k papers
- University of Chinese Academy of SciencesCN2.3k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT2.3k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR2.3k papers
- Rutherford Appleton LaboratoryGB2.2k papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT2.2k papers
- European Organization for Nuclear ResearchCH2.2k papers
65 papers · 2 filters
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Zhao Chen, Xiaodong Wang
Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload w…
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Sanjeev Arora, Zhiyuan Li, Kaifeng Lyu
Batch Normalization (BN) has become a cornerstone of deep learning across diverse architectures, appearing to help optimization as well as generalization. While the idea makes intu…
GAN-EM: GAN based EM learning framework
Wentian Zhao, Shaojie Wang, Zhihuai Xie +2
Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires…
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
Kunjin Chen, Jun Hu, Yu Zhang +2
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at…
Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquit…
Few Sample Knowledge Distillation for Efficient Network Compression
Tianhong Li, Jianguo Li, Zhuang Liu +1
Deep neural network compression techniques such as pruning and weight tensor decomposition usually require fine-tuning to recover the prediction accuracy when the compression ratio…
Policy Optimization with Model-based Explorations
Feiyang Pan, Qingpeng Cai, An-Xiang Zeng +5
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games…
Composite Binary Decomposition Networks
You Qiaoben, Zheng Wang, Jianguo Li +3
Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precisi…
Private Model Compression via Knowledge Distillation
Ji Wang, Weidong Bao, Lichao Sun +3
The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs noto…
Recent Research Advances on Interactive Machine Learning
Liu Jiang, Shixia Liu, Changjian Chen
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to ef…
Multi-armed Bandits with Compensation
Siwei Wang, Longbo Huang
We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for steps. In each step,…
Neural CRF transducers for sequence labeling
Kai Hu, Zhijian Ou, Min Hu +1
Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling. Various linear-chain neural CRFs (NCRFs) are developed to impleme…
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
Li Huang, Yifeng Yin, Zeng Fu +3
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many differen…
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
Bao Wang, Binjie Yuan, Zuoqiang Shi +1
Empirical adversarial risk minimization (EARM) is a widely used mathematical framework to robustly train deep neural nets (DNNs) that are resistant to adversarial attacks. However,…
An Efficient Approach to Informative Feature Extraction from Multimodal Data
Lichen Wang, Jiaxiang Wu, Shao-Lun Huang +4
One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the…
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Yunbo Wang, Jianjin Zhang, Hongyu Zhu +3
Natural spatiotemporal processes can be highly non-stationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel…
An initial attempt of combining visual selective attention with deep reinforcement learning
Liu Yuezhang, Ruohan Zhang, Dana H. Ballard
Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such…
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li +4
Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods…
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han, Hao Zhu, Pengfei Yu +4
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation…
Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
Zhengchao Zhang, Meng Li, Xi Lin +2
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynami…
Faster Matrix Completion Using Randomized SVD
Xu Feng, Wenjian Yu, Yaohang Li
Matrix completion is a widely used technique for image inpainting and personalized recommender system, etc. In this work, we focus on accelerating the matrix completion using faste…
Fast Randomized PCA for Sparse Data
Xu Feng, Yuyang Xie, Mingye Song +2
Principal component analysis (PCA) is widely used for dimension reduction and embedding of real data in social network analysis, information retrieval, and natural language process…
Sparse DNNs with Improved Adversarial Robustness
Yiwen Guo, Chao Zhang, Changshui Zhang +1
Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense…
Learning Two-layer Neural Networks with Symmetric Inputs
Rong Ge, Rohith Kuditipudi, Zhize Li +1
We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer network $$ y = A σ(Wx) + ξ,…