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
95 papers · 2 filters
Seizure Prediction Using Bidirectional LSTM
Hazrat Ali, Feroz Karim, Junaid Javed Qureshi +2
Approximately, 50 million people in the world are affected by epilepsy. For patients, the anti-epileptic drugs are not always useful and these drugs may have undesired side effects…
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan +2
Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, the low-rank structure is a global proper…
The Search for Sparse, Robust Neural Networks
Justin Cosentino, Federico Zaiter, Dan Pei +1
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters…
Direct and indirect reinforcement learning
Yang Guan, Shengbo Eben Li, Jingliang Duan +4
Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. In this paper, we classify RL into dir…
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
Zhuo Wang, Wei Zhang, Ning Liu +1
Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance,…
Attentive Representation Learning with Adversarial Training for Short Text Clustering
Wei Zhang, Chao Dong, Jianhua Yin +1
Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. Howeve…
Minimum Class Confusion for Versatile Domain Adaptation
Ying Jin, Ximei Wang, Mingsheng Long +1
There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-…
Less Is Better: Unweighted Data Subsampling via Influence Function
Zifeng Wang, Hong Zhu, Zhenhua Dong +2
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.…
Stable Learning via Sample Reweighting
Zheyan Shen, Peng Cui, Tong Zhang +1
We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of paramet…
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Yuanhao Wang, Guodong Zhang, Jimmy Ba
Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequential games, also known as minimax…
A Domain Adaptive Density Clustering Algorithm for Data with Varying Density Distribution
Jianguo Chen, Philip S. Yu
As one type of efficient unsupervised learning methods, clustering algorithms have been widely used in data mining and knowledge discovery with noticeable advantages. However, clus…
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Tunhou Zhang, Hsin-Pai Cheng, Zhenwen Li +4
Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limit…
CAT: CRF-based ASR Toolkit
Keyu An, Hongyu Xiang, Zhijian Ou
In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CAT (CRF-based ASR Toolkit). A key feature of CAT is discriminative training in th…
Vulnerability Analysis for Data Driven Pricing Schemes
Jingshi Cui, Haoxiang Wang, Chenye Wu +1
Data analytics and machine learning techniques are being rapidly adopted into the power system, including power system control as well as electricity market design. In this paper,…
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang +4
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves…
Adversarial Margin Maximization Networks
Ziang Yan, Yiwen Guo, Changshui Zhang
The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability. Unlike the human visual system which is able…
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs
Tong Wang, Fujie Jin, Yu Hu +1
Medical crowdfunding is a popular channel for people needing financial help paying medical bills to collect donations from large numbers of people. However, large heterogeneity exi…
Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang +3
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and…
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
Yikai Wang, Liang Zhang, Quanyu Dai +5
Improving the performance of click-through rate (CTR) prediction remains one of the core tasks in online advertising systems. With the rise of deep learning, CTR prediction models…
Policy-Gradient Training of Fair and Unbiased Ranking Functions
Himank Yadav, Zhengxiao Du, Thorsten Joachims
While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and attractive source of data for learning to rank, it can produce unfair ranking policies for both exogeno…
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei +3
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have r…
Multi-Path Policy Optimization
Ling Pan, Qingpeng Cai, Longbo Huang
Recent years have witnessed a tremendous improvement of deep reinforcement learning. However, a challenging problem is that an agent may suffer from inefficient exploration, partic…
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin +3
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-t…