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Publications (8 of 8) Show all publications
Zhang, C. & Hua, Q. (2016). Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine: Application of GEMs. Frontiers in Physiology, 6(January), Article ID 413.
Open this publication in new window or tab >>Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine: Application of GEMs
2016 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 6, no January, article id 413Article in journal (Refereed) Published
Abstract [en]

Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2016
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-248600 (URN)10.3389/fphys.2015.00413 (DOI)2-s2.0-84962176145 (Scopus ID)
Note

QC 20190412

Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-23Bibliographically approved
Qu, A., Zhang, C., Ackermann, P. & Kjellström, H. (2016). Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation. In: : . Paper presented at NIPS Workshop on Machine Learning for Health.
Open this publication in new window or tab >>Bridging Medical Data Inference to Achilles Tendon Rupture Rehabilitation
2016 (English)Conference paper, Poster (with or without abstract) (Refereed)
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-197302 (URN)
Conference
NIPS Workshop on Machine Learning for Health
Note

QC 20161208

Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2016-12-08Bibliographically approved
Zhang, C. (2016). Structured Representation Using Latent Variable Models. (Doctoral dissertation). Stockholm: KTH Royal Institute of Technology
Open this publication in new window or tab >>Structured Representation Using Latent Variable Models
2016 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Over the past two centuries the industrial revolution automated a great part of work that involved human muscles. Recently, since the beginning of the 21st century, the focus has shifted towards automating work that is involving our brain to further improve our lives. This is accomplished by establishing human-level intelligence through machines, which lead to the growth of the field of artificial intelligence. Machine learning is a core component of artificial intelligence. While artificial intelligence focuses on constructing an entire intelligence system, machine learning focuses on the learning ability and the ability to further use the learned knowledge for different tasks. This thesis targets the field of machine learning, especially structured representation learning, which is key for various machine learning approaches.

Humans sense the environment, extract information and make action decisions based on abstracted information. Similarly, machines receive data, abstract information from data through models and make decisions about the unknown through inference. Thus, models provide a mechanism for machines to abstract information. This commonly involves learning useful representations which are desirably compact, interpretable and useful for different tasks. In this thesis, the contribution relates to the design of efficient representation models with latent variables. To make the models useful, efficient inference algorithms are derived to fit the models to data. We apply our models to various applications from different domains, namely E-health, robotics, text mining, computer vision and recommendation systems.

The main contribution of this thesis relates to advancing latent variable models and deriving associated inference schemes for representation learning. This is pursued in three different directions. Firstly, through supervised models, where better representations can be learned knowing the tasks, corresponding to situated knowledge of humans. Secondly, through structured representation models, with which different structures, such as factorized ones, are used for latent variable models to form more efficient representations. Finally, through non-parametric models, where the representation is determined completely by the data. Specifically, we propose several new models combining supervised learning and factorized representation as well as a further model combining non-parametric modeling and supervised approaches. Evaluations show that these new models provide generally more efficient representations and a higher degree of interpretability.

Moreover, this thesis contributes by applying these proposed models in different practical scenarios, demonstrating that these models can provide efficient latent representations. Experimental results show that our models improve the performance for classical tasks, such as image classification and annotations, robotic scene and action understanding. Most notably, one of our models is applied to a novel problem in E-health, namely diagnostic prediction using discomfort drawings. Experimental investigation show here that our model can achieve significant results in automatic diagnosing and provides profound understanding of typical symptoms. This motivates novel decision support systems for healthcare personnel.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. p. 245
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2016:18
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-191455 (URN)978-91-7729-080-3 (ISBN)
Public defence
2016-09-26, F3, Lindstedtsvägen 26, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

QC 20160905

Available from: 2016-09-05 Created: 2016-08-30 Last updated: 2016-10-26Bibliographically approved
Zhang, C., Gartrell, M., Minka, T. P. P., Zaykov, Y. & Guiver, J. (2015). GroupBox: A generative model for group recommendation. Microsoft Research
Open this publication in new window or tab >>GroupBox: A generative model for group recommendation
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2015 (English)Report (Refereed)
Abstract [en]

In this paper, we present a principled probabilistic framework – GroupBox – for making recommendations to groups. GroupBox is able to model user influence within a group, the suitability of an item to a group context, and the differences in user preference between individual and group contexts. Efficient scalable inference algorithms are used for GroupBox, which makes it applicable to large-scale datasets. We run experiments on a large-scale TV viewing dataset collected by Nielsen and show how the model can be used to understand both context and influence. The experimental results on the large scale real data provide a deep understanding of the individual behaviours in group context.

Place, publisher, year, edition, pages
Microsoft Research, 2015. p. 8
Series
TechReport ; MSR-TR–2015-61
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-171967 (URN)
Note

QC 20150811

Available from: 2015-08-10 Created: 2015-08-10 Last updated: 2015-08-11Bibliographically approved
Zhang, C. & Kjellström, H. (2014). How to Supervise Topic Models. In: Agapito, Bronstein, Rother (Ed.), Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. Paper presented at European Conference on Computer Vision (ECCVws 2014, GMCV),Zurich, September 6-12, 2014 (pp. 500-515). Paper presented at European Conference on Computer Vision (ECCVws 2014, GMCV),Zurich, September 6-12, 2014. Zurich: Springer Publishing Company
Open this publication in new window or tab >>How to Supervise Topic Models
2014 (English)In: Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II / [ed] Agapito, Bronstein, Rother, Zurich: Springer Publishing Company, 2014, p. 500-515Chapter in book (Refereed)
Abstract [en]

Supervised topic models are important machine learning tools whichhave been widely used in computer vision as well as in other domains. However,there is a gap in the understanding of the supervision impact on the model. Inthis paper, we present a thorough analysis on the behaviour of supervised topicmodels using Supervised Latent Dirichlet Allocation (SLDA) and propose twofactorized supervised topic models, which factorize the topics into signal andnoise. Experimental results on both synthetic data and real-world data for computer vision tasks show that supervision need to be boosted to be effective andfactorized topic models are able to enhance the performance.

Place, publisher, year, edition, pages
Zurich: Springer Publishing Company, 2014
Keywords
Topic Modeling, SLDA, LDA, Factorized Supervised Topic Models
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-152691 (URN)10.1007/978-3-319-16181-5_39 (DOI)000362495500039 ()2-s2.0-84928801474 (Scopus ID)978-3-319-16181-5 (ISBN)
Conference
European Conference on Computer Vision (ECCVws 2014, GMCV),Zurich, September 6-12, 2014
Funder
Swedish Research Council
Note

QC 20141024

Available from: 2014-10-01 Created: 2014-10-01 Last updated: 2018-01-11Bibliographically approved
Zhang, C., Song, D. & Kjellström, H. (2013). Contextual Modeling with Labeled Multi-LDA. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): . Paper presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-8, 2013 at Tokyo Big Sight, Japan (pp. 2264-2271). IEEE
Open this publication in new window or tab >>Contextual Modeling with Labeled Multi-LDA
2013 (English)In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE , 2013, p. 2264-2271Conference paper, Published paper (Refereed)
Abstract [en]

Learning about activities and object affordances from human demonstration are important cognitive capabilities for robots functioning in human environments, for example, being able to classify objects and knowing how to grasp them for different tasks. To achieve such capabilities, we propose a Labeled Multi-modal Latent Dirichlet Allocation (LM-LDA), which is a generative classifier trained with two different data cues, for instance, one cue can be traditional visual observation and another cue can be contextual information. The novel aspects of the LM-LDA classifier, compared to other methods for encoding contextual information are that, I) even with only one of the cues present at execution time, the classification will be better than single cue classification since cue correlations are encoded in the model, II) one of the cues (e.g., common grasps for the observed object class) can be inferred from the other cue (e.g., the appearance of the observed object). This makes the method suitable for robot online and transfer learning; a capability highly desirable in cognitive robotic applications. Our experiments show a clear improvement for classification and a reasonable inference of the missing data.

Place, publisher, year, edition, pages
IEEE, 2013
Series
IEEE International Conference on Intelligent Robots and Systems, ISSN 2153-0858
Keywords
LDA, Topic Model, Contextual Modeling
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-134127 (URN)10.1109/IROS.2013.6696673 (DOI)000331367402063 ()2-s2.0-84893758693 (Scopus ID)
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), November 3-8, 2013 at Tokyo Big Sight, Japan
Note

QC 20131217

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2019-04-26Bibliographically approved
Zhang, C., Ek, C. H., Damianou, A. & Kjellström, H. (2013). Factorized Topic Models. In: : . Paper presented at International Conference on Learning Representations.
Open this publication in new window or tab >>Factorized Topic Models
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a modification to a latent topic model, which makes themodel exploit supervision to produce a factorized representation of the observeddata. The structured parameterization separately encodes variance that is sharedbetween classes from variance that is private to each class by the introduction of anew prior over the topic space. The approach allows for a more efficient inferenceand provides an intuitive interpretation of the data in terms of an informative signaltogether with structured noise. The factorized representation is shown to enhanceinference performance for image, text, and video classification.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-134126 (URN)
Conference
International Conference on Learning Representations
Note

QC 20131217

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2018-01-11Bibliographically approved
Zhang, C., Ek, C. H., Gratal, X., Pokorny, F. T. & Kjellström, H. (2013). Supervised Hierarchical Dirichlet Processes with Variational Inference. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW): . Paper presented at 2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013; Sydney, NSW; Australia; 1 December 2013 through 8 December 2013 (pp. 254-261). IEEE
Open this publication in new window or tab >>Supervised Hierarchical Dirichlet Processes with Variational Inference
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2013 (English)In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), IEEE , 2013, p. 254-261Conference paper, Published paper (Refereed)
Abstract [en]

We present an extension to the Hierarchical Dirichlet Process (HDP), which allows for the inclusion of supervision. Our model marries the non-parametric benefits of HDP with those of Supervised Latent Dirichlet Allocation (SLDA) to enable learning the topic space directly from data while simultaneously including the labels within the model. The proposed model is learned using variational inference which allows for the efficient use of a large training dataset. We also present the online version of variational inference, which makes the method scalable to very large datasets. We show results comparing our model to a traditional supervised parametric topic model, SLDA, and show that it outperforms SLDA on a number of benchmark datasets.

Place, publisher, year, edition, pages
IEEE, 2013
Keywords
Topic Modeling, HDP, Supervised HDP, Dirichlet Processes
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-134128 (URN)10.1109/ICCVW.2013.41 (DOI)000349847200036 ()2-s2.0-84897533026 (Scopus ID)
Conference
2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013; Sydney, NSW; Australia; 1 December 2013 through 8 December 2013
Note

QC 20131217

Available from: 2013-11-18 Created: 2013-11-18 Last updated: 2019-04-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8640-9370

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