224 citations · 3.2k across the 170 of their papers we have counts for
14 papers · 1 filter
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Yang Song, Jun Zhu
Bayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results. However, little work has been done on Bayesian matrix compl…
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…
Jointly Modeling Topics and Intents with Global Order Structure
Bei Chen, Jun Zhu, Nan Yang +3
Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modelin…
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
Jiaxin Shi, Jun Zhu
We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning proces…
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization
Jun Zhu, Xianjie Chen, Alan L. Yuille
In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 obj…
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
Jianfei Chen, Kaiwei Li, Jun Zhu +1
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hasting…
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Shaohua Li, Jun Zhu, Chunyan Miao
Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic in…
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…
Pose-Guided Human Parsing with Deep Learned Features
Fangting Xia, Jun Zhu, Peng Wang +1
Parsing human body into semantic regions is crucial to human-centric analysis. In this paper, we propose a segment-based parsing pipeline that explores human pose information, i.e.…
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…
Bounded-Distortion Metric Learning
Renjie Liao, Jianping Shi, Ziyang Ma +2
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering. Although a greatly distorted metric space has a high degree of free…
Fast Sampling for Bayesian Max-Margin Models
Wenbo Hu, Jun Zhu, Bo Zhang
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowd…
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…