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- Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu
- PLoS computational biology
- 2017

MOTIVATION
Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de… (More)

- Siqi Sun, Mladen Kolar, Jinbo Xu
- NIPS
- 2015

Learning the structure of a probabilistic graphical models is a well studied problem in the machine learning community due to its importance in many applications. Current approaches are mainly focused on learning the structure under restrictive parametric assumptions, which limits the applicability of these methods. In this paper, we study the problem of… (More)

- Siqi Sun, Xinran Dong, Yao Fu, Weidong Tian
- Nucleic acids research
- 2012

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity… (More)

- Qingming Tang, Siqi Sun, Jinbo Xu
- ICML
- 2015

Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a… (More)

- Branislav Kveton, Hung Hai Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun
- ECML/PKDD
- 2016

Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables. The count-min (CM) sketch is a popular approach to estimating probabilities in high-cardinality data but it does not… (More)

Learning the structure of a graphical model is a fundamental problem and it is used extensively to infer the relationship between random variables. In many real world applications, we usually have some prior knowledge about the underlying graph structure, such as degree distribution and block structure. In this paper, we propose a novel generative model for… (More)

- Sheng Wang, Siqi Sun, Jinbo Xu
- ECML/PKDD
- 2016

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood… (More)

- Qingming Tang, Siqi Sun, Chao Yang, Jinbo Xu
- ArXiv
- 2015

Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which are widely used to model social and biological networks. The prior not only favors a desirable global node degree distribution, but also takes into consideration the relative… (More)

- Siqi Sun, Yuancheng Zhu, Jinbo Xu
- AISTATS
- 2014

Gaussian graphical models (GGMs) are widely-used to describe the relationship between random variables. In many real-world applications, GGMs have a block structure in the sense that the variables can be clustered into groups so that inter-group correlation is much weaker than intra-group correlation. We present a novel nonparametric Bayesian generative… (More)

- Sheng Wang, Siqi Sun, Jinbo Xu
- ArXiv
- 2015

Learning from complex data with imbalanced label distribution is a challenging problem, especially when the data/label form structure, such as linearchain or tree-like. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced structured data. To model the complex relationship between the… (More)