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
18 papers · 2 filters
Learning Sparse Visual Representations with Leaky Capped Norm Regularizers
Jianqiao Wangni, Dahua Lin
Sparsity inducing regularization is an important part for learning over-complete visual representations. Despite the popularity of regularization, in this paper, we invest…
Structured Generative Adversarial Networks
Zhijie Deng, Hao Zhang, Xiaodan Liang +4
We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labele…
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
Yucen Luo, Jun Zhu, Mengxi Li +2
The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under diff…
Energy-efficient Amortized Inference with Cascaded Deep Classifiers
Jiaqi Guan, Yang Liu, Qiang Liu +1
Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.…
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
Bokai Cao, Mia Mao, Siim Viidu +1
On electronic game platforms, different payment transactions have different levels of risk. Risk is generally higher for digital goods in e-commerce. However, it differs based on p…
Multi-Entity Dependence Learning with Rich Context via Conditional Variational Auto-encoder
Luming Tang, Yexiang Xue, Di Chen +1
Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme…
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering
Yanxi Chen, Gen Li, Yuantao Gu
Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering high-dimensional data points lying in a union of low-dimensional subspaces. However, while opt…
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
Xinyue Shen, Yuantao Gu
In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly conv…
Partial Transfer Learning with Selective Adversarial Networks
Zhangjie Cao, Mingsheng Long, Jianmin Wang +1
Adversarial learning has been successfully embedded into deep networks to learn transferable features, which reduce distribution discrepancy between the source and target domains.…
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Yuzhi Wang, Anqi Yang, Xiaoming Chen +3
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs). With the proliferation of large-scale and long-term WSNs, it is be…
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
Lijie Chen, Anupam Gupta, Jian Li +2
We study the combinatorial pure exploration problem Best-Set in stochastic multi-armed bandits. In a Best-Set instance, we are given arms with unknown reward distributions, as…
Hidden Talents of the Variational Autoencoder
Bin Dai, Yu Wang, John Aston +2
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an informatio…
Towards Robust Detection of Adversarial Examples
Tianyu Pang, Chao Du, Yinpeng Dong +1
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples. In this paper, we present a novel training p…
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
Xiudong Wang, Yuantao Gu
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the diction…
GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework
Lei Deng, Peng Jiao, Jing Pei +2
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end,…
Snapshot Ensembles: Train 1, get M for free
Gao Huang, Yixuan Li, Geoff Pleiss +3
Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally…
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk
Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks. In this paper, a novel approach is proposed whi…
HashNet: Deep Learning to Hash by Continuation
Zhangjie Cao, Mingsheng Long, Jianmin Wang +1
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep…