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
13 papers · 2 filters
Fast Parallel SVM using Data Augmentation
Hugh Perkins, Minjie Xu, Jun Zhu +1
As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed…
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
Bei Chen, Ning Chen, Jun Zhu +2
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic…
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander G. Schwing, Richard S. Zemel +1
Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the appl…
Harmonic Extension
Zuoqiang Shi, Jian Sun, Minghao Tian
In this paper, we consider the harmonic extension problem, which is widely used in many applications of machine learning. We find that the transitional method of graph Laplacian fa…
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Bokai Cao, Xiangnan Kong, Jingyuan Zhang +2
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc.…
Dropout Training for SVMs with Data Augmentation
Ning Chen, Jun Zhu, Jianfei Chen +1
Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and em…
Max-Entropy Feed-Forward Clustering Neural Network
Han Xiao, Xiaoyan Zhu
The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into c…
Learning Deep Generative Models with Doubly Stochastic MCMC
Chao Du, Jun Zhu, Bo Zhang
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter sp…
Learning Multiple Tasks with Multilinear Relationship Networks
Mingsheng Long, Zhangjie Cao, Jianmin Wang +1
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific alo…
Learning Arbitrary Statistical Mixtures of Discrete Distributions
Jian Li, Yuval Rabani, Leonard J. Schulman +1
We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, , is a proba…
Max-margin Deep Generative Models
Chongxuan Li, Jun Zhu, Tianlin Shi +1
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…
Learning Transferable Features with Deep Adaptation Networks
Mingsheng Long, Yue Cao, Jianmin Wang +1
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually tr…
Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition
Qi Guo, Bo-Wei Chen, Feng Jiang +2
This study presents a divide-and-conquer (DC) approach based on feature space decomposition for classification. When large-scale datasets are present, typical approaches usually em…