Keyword: machine learning

2019 (2)
Applications of machine learning in real-life digital health interventions: Review of the literature. Triantafyllidis, A., K. and Tsanas, A. Journal of Medical Internet Research, 21(4):1-9, 2019.
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Semi-Supervised Learning using Differentiable Reasoning. van Krieken, E.; Acar, E.; and van Harmelen, F. IFCoLog Journal of Logic and its Applications, 6(4):633-653, 2019.
Semi-Supervised Learning using Differentiable Reasoning [pdf]Paper  bibtex   2 downloads  
2018 (13)
Multiobjective Optimization for Stiffness and Position Control in a Soft Robot Arm Module. Ansari, Y.; Manti, M.; Falotico, E.; Cianchetti, M.; and Laschi, C. IEEE Robotics and Automation Letters, 3(1):108–115, January, 2018.
doi  abstract   bibtex   
Block Stability for MAP Inference. Lang, H.; Sontag, D.; and Vijayaraghavan, A. ArXiv e-prints arXiv:1810.05305, 2018.
Block Stability for MAP Inference [link]Paper  abstract   bibtex   
Gaze and the Control of Foot Placement When Walking in Natural Terrain. Matthis, J. S.; Yates, J. L.; and Hayhoe, M. M. Current Biology, April, 2018.
Gaze and the Control of Foot Placement When Walking in Natural Terrain [link]Paper  doi  bibtex   
Learning Topic Models - Provably and Efficiently. Arora, S.; Ge, R.; Halpern, Y.; Mimno, D.; Moitra, A.; Sontag, D.; Wu, Y.; and Zhu, M. Communications of the ACM, 61(4):85-93, 2018.
Learning Topic Models - Provably and Efficiently [link]Paper  bibtex   1 download  
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques. Seixas, A., A.; Henclewood, D., A.; Williams, S., K.; Jagannathan, R.; Ramos, A.; Zizi, F.; and Jean-Louis, G. Frontiers in Neurology, 9:534, Frontiers, 7, 2018.
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques [link]Website  abstract   bibtex   
Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds. Khater, I. M.; Meng, F.; Wong, T. H.; Nabi, I. R.; and Hamarneh, G. Nature - Scientific reports, 8(9009):1-15, 2018.
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Semi-Amortized Variational Autoencoders. Kim, Y.; Wiseman, S.; Miller, A. C.; Sontag, D.; and Rush, A. M. In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
Semi-Amortized Variational Autoencoders [pdf]Paper  abstract   bibtex   
seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data. Chen, Z.; Quan, L.; Huang, A.; Zhao, Q.; Yuan, Y.; Yuan, X.; Shen, Q.; Shang, J.; Ben, Y.; Qin, F. X.; and Wu, A. Frontiers in Immunology, 9:1286, 2018.
doi  abstract   bibtex   
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. Carrillo, F.; Sigman, M.; Fernández Slezak, D.; Ashton, P.; Fitzgerald, L.; Stroud, J.; Nutt, D., J.; and Carhart-Harris, R., L. Journal of Affective Disorders, 230:84-86, Elsevier, 4, 2018.
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression [link]Website  abstract   bibtex   
Noninvasive Determination of Gene Mutations in Clear Cell Renal Cell Carcinoma using Multiple Instance Decisions Aggregated CNN. Hussain, A.; Hamarneh, G.; and Abugharbieh, R. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11071, pages 657-665, 2018.
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Generative Adversarial Networks to Segment Skin Lesions. Izadi, S.; Mirikharaji, Z.; Kawahara, J.; and Hamarneh, G. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 881-884, 2018.
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Stroke-Associated Hemiparesis Detection Using Body Joints and Support Vector Machines. Ramesh, V.; Agrawal, K.; Meyer, B.; Cauwenberghs, G.; and Weibel, N. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, of PervasiveHealth '18, pages 55–58, New York, NY, USA, 2018. ACM.
Stroke-Associated Hemiparesis Detection Using Body Joints and Support Vector Machines [link]Paper  doi  abstract   bibtex   
Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation. Mirikharaji, Z. and Hamarneh, G. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11073, pages 737-745, 2018.
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2017 (14)
BrainNetCNN: Artificial Convolutional Neural Networks for Connectomes. Kawahara, J.; Brown, C. J.; Miller, S.; Booth, B. G.; Chau, V.; Grunau, R.; Zwicker, J.; and Hamarneh, G. In 2nd Annual Health Technology Symposium, Vancouver, Canada, pages 1, 2017.
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Deep Learning for Health Informatics. Ravi, D.; Wong, C.; Deligianni, F.; Berthelot, M.; Andreu-Perez, J.; Lo, B.; and Yang, G. IEEE Journal of Biomedical and Health Informatics, 21(1):4–21, January, 2017.
Deep Learning for Health Informatics [link]Paper  doi  bibtex   
Lesion volume Estimation from PET without Requiring Segmentation. Taghanaki, S. A.; Duggan, N.; Ma, H.; Celler, A.; Benard, F.; and Hamarneh, G. In Quantitative Imaging Network (QIN) Annual Meeting, 2017.
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Learning to Run Heuristics in Tree Search. Nargesian, F.; Samulowitz, H.; Khurana, U.; Khalil, E. B.; and Turaga, D. In 26th International Joint Conference on Artificial Intelligence, 2017.
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DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. Rajchl, M.; Lee, M. C. H.; Oktay, O.; Kamnitsas, K.; Passerat-Palmbach, J.; Bai, W.; Damodaram, M.; Rutherford, M. A.; Hajnal, J. V.; Kainz, B.; and Rueckert, D. IEEE Trans. Med. Imaging, 36(2):674–683, 2017.
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks [link]Paper  doi  bibtex   
Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression. Chang, A. H; Hubicki, C. M; Aguilar, J. J; Goldman, D. I; Ames, A. D; and Vela, P. A In Robotics and Automation (ICRA), 2017 IEEE International Conference on, pages 2154–2160, 2017. IEEE.
Learning to jump in granular media: Unifying optimal control synthesis with Gaussian process-based regression [pdf]Paper  bibtex   1 download  
Fully Convolutional Networks to Detect Clinical Dermoscopic Features. Kawahara, J. and Hamarneh, G. Technical Report arxiv:1703.04559, 3, 2017.
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A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling. Arabnejad, H.; Pahl, C.; Jamshidi, P.; and Estrada, G. In Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Madrid, Spain, May 14-17, 2017, pages 64–73, 2017.
A Comparison of Reinforcement Learning Techniques for Fuzzy Cloud Auto-Scaling [link]Paper  doi  bibtex   
Exploring Stroke-associated Hemiparesis Assessment with Support Vector Machines. Ramesh, V.; Agrawal, K.; Meyer, B.; Cauwenberghs, G.; and Weibel, N. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare, of PervasiveHealth '17, pages 464–467, New York, NY, USA, 2017. ACM.
Exploring Stroke-associated Hemiparesis Assessment with Support Vector Machines [link]Paper  doi  abstract   bibtex   
Infusing Latent User-Concerns from User Reviews into Collaborative Filtering. Pradhan, L.; Zhang, C.; and Bethard, S. In 2017 IEEE International Conference on Information Reuse and Integration (IRI), pages 471-477, 8, 2017.
Infusing Latent User-Concerns from User Reviews into Collaborative Filtering [link]Paper  doi  bibtex   
Machine-learning techniques in economics : New Tools for Predicting Economic Growth. Basuchoudhary, A.; Bang, J., T.; and Sen, T. Springer, Cham, 2017.
abstract   bibtex   
A Practical Guide To Using Face Technology (Part I). Lee, F. November, 2017.
A Practical Guide To Using Face Technology (Part I) [link]Paper  abstract   bibtex   
Learning Feature Engineering for Classification. Nargesian, F.; Samulowitz, H.; Khurana, U.; Khalil, E. B.; and Turaga, D. In 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
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2016 (15)
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. Schrider, D. R. and Kern, A. D. PLOS Genetics, 12(3):e1005928, March, 2016.
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning [link]Paper  doi  abstract   bibtex   
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks. Geitgey, A. June, 2016.
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks [link]Paper  abstract   bibtex   
Discovering Biosignatures of Cav1 Domains: Computational Methods for Super-resolution Microscopy. Khater, I. M.; Meng, F.; Nabi, I. R.; and Hamarneh, G. In LSI Imaging Super-resolution Mini-symposium, Vancouver, Canada, pages 1, 2016.
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Clinically-Inspired Automatic Classification of Ovarian Carcinoma Subtypes. BenTaieb, A.; Nosrati, M.; Li-Chang, H.; Huntsman, D.; and Hamarneh, G. Journal of Pathology Informatics, 7(1):1-28, 2016.
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Automated detection and classification of feeding strikes by larval fish from continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events. Zilka, M.; Eyal Shamur, E.; Hassner, T.; China, V.; Liberzon, A.; and Holzman, R. Journal of Experimental Biology, 2016.
Automated detection and classification of feeding strikes by larval fish from continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events [pdf]Paper  abstract   bibtex   
Learning to Branch in Mixed Integer Programming. Khalil, E. B.; Le Bodic, P.; Song, L.; Nemhauser, G. L; and Dilkina, B. N In AAAI, pages 724–731, 2016.
Learning to Branch in Mixed Integer Programming. [pdf]Paper  bibtex   
Backcasting and a new way of command in computational design. Koenig, R. and Schmitt, G. In CAADence in Architecture, pages 15–25, 2016.
doi  abstract   bibtex   
Tumour Lesion Segmentation from 3D PET using a Machine Learning driven Active Surface. Ahmadvand, P.; Duggan, N.; Benard, F.; and Hamarneh, G. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 10019, pages 271-278, 2016.
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A Data-Driven Demand Response Recommender System. Behl, M. and Mangharam, R. Journal of Applied Energy, 2016. [Under Review]
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Deriving the Geographic Footprint of Cognitive Regions. Hobel, H.; Fogliaroni, P.; and Frank, A. U In Sarjakoski, T.; Santos, M. Y.; and Sarjakoski, T., editors, International Conference on Geographic Information Science (AGILE 2016), Helsinki (Finland), of Lecture Notes in Geoinformation and Cartography, pages 67–84, 2016. Springer.
Deriving the Geographic Footprint of Cognitive Regions [link]Paper  doi  abstract   bibtex   
Predictive Subnetwork Extraction with Structural Priors for Infant Connectomes. Brown, C. J.; Miller, S.; Booth, B. G.; Zwicker, J.; Grunau, R.; Synnes, A.; Chau, V.; and Hamarneh, G. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9900, pages 175-183, 2016.
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Learning Time-Varying Forecast Combinations. Mandel, A. and Sani, A. Working Paper Centre d'Economie de la Sorbonne 2016.36, 2016.
Learning Time-Varying Forecast Combinations [link]Paper  abstract   bibtex   
Comparing of feature selection and classification methods on report-based subhealth data. Li Huang; Shixing Yan; Jiamin Yuan; Zhiya Zuo; Fuping Xu; Yanzhao Lin; Mary Qu Yang; Zhimin Yang; and Li, G. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 1356-1358, 12, 2016. IEEE.
Comparing of feature selection and classification methods on report-based subhealth data [link]Website  abstract   bibtex   
On the Application of Rough Sets to Skeletal Maturation Classification. Garza-Morales, R.; López-Irarragori, F.; and Sanchez, R. Artif. Intell. Rev., 45(4):489--508, Kluwer Academic Publishers, Norwell, MA, USA, April, 2016.
On the Application of Rough Sets to Skeletal Maturation Classification [link]Paper  doi  abstract   bibtex   
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures. Jamshidi, P.; Sharifloo, A. M.; Pahl, C.; Arabnejad, H.; Metzger, A.; and Estrada, G. In 12th International ACM SIGSOFT Conference on Quality of Software Architectures, QoSA 2016, Venice, Italy, April 5-8, 2016, pages 70–79, 2016. $\bigstar$
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [pdf]Paper  Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [link]Slides  Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Architectures [link]Paper  doi  bibtex   
2015 (8)
Probabilistic event calculus for event recognition. Skarlatidis, A.; Paliouras, G.; Artikis, A.; and Vouros, G. A. ACM Trans. Comput. Logic, 16(2):11:1--11:37, ACM, New York, NY, USA, feb, 2015.
Probabilistic event calculus for event recognition [link]Paper  doi  bibtex   
Grand Challenge Veterinary Imaging: Technology, Science, and Communication. McEvoy, F., J. Frontiers in veterinary science, 2:38, 9, 2015.
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Metadata Dependent Mondrian Processes. Wang, Y.; Li, B.; Wang, Y.; and Chen, F. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, of ICML'15, pages 1339--1347, 2015. JMLR.org.
Metadata Dependent Mondrian Processes [link]Paper  bibtex   
Exact algorithms for 2-clustering with size constraints in the Euclidean plane. Bertoni, A.; Goldwurm, M.; and Lin, J. In International Conference on Current Trends in Theory and Practice of Informatics, pages 128–139, 2015. Springer, Berlin, Heidelberg.
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On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer. Kop, R.; Hoogendoorn, M.; Moons; G, L. M; Numans; E, M.; and ten Teije, A. In Artificial Intelligence in Medicine, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015, Proceedings AIME, 2015. Springer.
On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer [pdf]Paper  abstract   bibtex   
Anchored Discrete Factor Analysis. Halpern, Y.; Horng, S.; and Sontag, D. In arXiv:1511.03299, 2015.
Anchored Discrete Factor Analysis [pdf]Paper  abstract   bibtex   
Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Diggins, K. E.; Jr., F.; Brent, P.; and Irish, J. M. Methods, 82:55--63, July, 2015.
Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data [link]Paper  doi  abstract   bibtex   
Accurate Data Cleansing through Model Checking and Machine Learning Techniques. Boselli, R.; Cesarini, M.; Mercorio, F.; and Mezzanzanica, M. In Helfert, M.; Holzinger, A.; Belo, O.; and Francalanci, C., editors, Data Management Technologies and Applications, volume 178, of Communications in Computer and Information Science, pages 62-80. Springer International Publishing, 2015.
Accurate Data Cleansing through Model Checking and Machine Learning Techniques [link]Paper  doi  bibtex   
2014 (7)
Instance Segmentation of Indoor Scenes using a Coverage Loss. Silberman, N.; Sontag, D.; and Fergus, R. In Fleet, D. J.; Pajdla, T.; Schiele, B.; and Tuytelaars, T., editors, Proceedings of the 13th European Conference on Computer Vision (ECCV), volume 8689, of Lecture Notes in Computer Science, pages 616–631, 2014. Springer.
Instance Segmentation of Indoor Scenes using a Coverage Loss [pdf]Paper  abstract   bibtex   
Descending-Path Convolution Kernel for Syntactic Structures. Lin, C.; Miller, T.; Kho, A.; Bethard, S.; Dligach, D.; Pradhan, S.; and Savova, G. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 81–86, Baltimore, Maryland, 6, 2014. Association for Computational Linguistics. [Acceptance rate 25%]
Descending-Path Convolution Kernel for Syntactic Structures [link]Paper  bibtex   
ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory. Švaco, M. and Jerbić, B. Advances in Artificial Neural Systems, 2014:1–9, 2014.
doi  bibtex   
Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues. Kukurba, K. R.; Zhang, R.; Li, X.; Smith, K. S.; Knowles, D. A.; How Tan, M.; Piskol, R.; Lek, M.; Snyder, M.; MacArthur, D. G.; Li, J. B.; and Montgomery, S. B. PLoS Genetics, 10(5):e1004304, Public Library of Science, 2014.
Allelic Expression of Deleterious Protein-Coding Variants across Human Tissues [link]Paper  doi  abstract   bibtex   
Software Bug Localization with Markov Logic. Zhang, S. 2014.
Software Bug Localization with Markov Logic [pdf]Paper  bibtex   
Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. Li, X.; Battle, A.; Karczewski, K. J.; Zappala, Z.; Knowles, D. A.; Smith, K. S.; Kukurba, K. R.; Wu, E.; Simon, N.; and Montgomery, S. B. American Journal of Human Genetics, 95(3):245–56, Elsevier, 2014.
Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants. [link]Paper  doi  abstract   bibtex   
A machine learning method for high-frequency data forecasting. Allende, H.; López, E.; and Allende-Cid, H. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 8827, pages 621-628, 2014.
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2013 (6)
Fixed-form variational posterior approximation through stochastic linear regression. Salimans, T. and Knowles, D. A. Bayesian Analysis, 8(4):837–882, International Society for Bayesian Analysis, 2013. Winner of the Lindley Prize!
Fixed-form variational posterior approximation through stochastic linear regression [link]Paper  doi  abstract   bibtex   
A support vector machine for terrain classification in on-demand deployments of wireless sensor networks. Haber, R.; Peter, A. M.; Otero, C. E.; Kostanic, I.; and Ejnioui, A. In Systems Conference (SysCon), 2013 IEEE International, pages 841–846, 2013. IEEE.
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Decision Forests with Spatio-temporal Features for Graph-based Tumour Segmentation in 4D Lung CT. Mirzaei, H.; Tang, L. Y. W.; Werner, R.; and Hamarneh, G. In Medical Image Computing and Computer-Assisted Intervention Workshop on Machine Learning in Medical Imaging (MICCAI MLMI), volume 8184, pages 179-186, 2013.
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Detecting inappropriate access to electronic health records using collaborative filtering. Menon, A. K.; Jiang, X.; Kim, J.; Vaidya, J.; and Ohno-Machado, L. Machine Learning, 95(1):87--101, Springer Nature, jun, 2013.
Detecting inappropriate access to electronic health records using collaborative filtering [link]Paper  doi  bibtex   
Parallel globally optimal structure learning of Bayesian networks. Nikolova, O.; Zola, J.; and Aluru, S. Journal of Parallel and Distributed Computing, 73(8):1039-1048, 8, 2013.
Parallel globally optimal structure learning of Bayesian networks [link]Website  abstract   bibtex   
Online ridge regression method using sliding windows. Arce, P. and Salinas, L. C. In pages 87-90, 2013.
doi  abstract   bibtex   
2012 (7)
Locally-Adaptive Similarity Metric for Deformable Medical Image Registration. Tang, L. Y. W.; Hero, A. O; and Hamarneh, G. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 728-731, 2012.
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Transfer learning for cross-company software defect prediction. Ma, Y.; Luo, G.; Zeng, X.; and Chen, A. Information and Software Technology, 54(3):248--256, 2012.
Transfer learning for cross-company software defect prediction [link]Paper  doi  abstract   bibtex   
Learning and Inference in Probabilistic Classifier Chains with Beam Search. Kumar, A.; Vembu, S.; Menon, A. K.; and Elkan, C. In Machine Learning and Knowledge Discovery in Databases, pages 665--680. Springer Berlin Heidelberg, 2012.
Learning and Inference in Probabilistic Classifier Chains with Beam Search [link]Paper  doi  bibtex   
Learning Features for Streak Detection in Dermoscopic Color Images using Localized Radial Flux of Principal Intensity Curvature. Mirzaalian, H.; Lee, T.; and Hamarneh, G. In IEEE workshop on Mathematical Methods for Biomedical Image Analysis (IEEE MMBIA), pages 97-101, 2012.
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An Infinite Latent Attribute Model for Network Data. Palla, K.; Knowles, D. A.; and Ghahramani, Z. In 29th International Conference on Machine Learning (ICML 2012), pages 1607–1614, 2012.
An Infinite Latent Attribute Model for Network Data [pdf]Paper  abstract   bibtex   
Intelligent system for predicting wireless sensor network performance in on-demand deployments. Otero, C.; Kostanic, I.; Peter, A.; Ejnioui, A.; and Daniel Otero, L. In 2012 IEEE Conference on Open Systems, ICOS 2012, 2012.
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Introduction to Dual Decomposition for Inference. Sontag, D.; Globerson, A.; and Jaakkola, T. In Sra, S.; Nowozin, S.; and Wright, S. J., editors, Optimization for Machine Learning, pages 219–254. MIT Press, 2012.
Introduction to Dual Decomposition for Inference [pdf]Paper  abstract   bibtex   
2011 (3)
Response prediction using collaborative filtering with hierarchies and side-information. Menon, A. K.; Chitrapura, K.; Garg, S.; Agarwal, D.; and Kota, N. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD \textquotesingle11, 2011. ACM Press.
Response prediction using collaborative filtering with hierarchies and side-information [link]Paper  doi  bibtex   
Fast Algorithms for Approximating the Singular Value Decomposition. Menon, A. K. and Elkan, C. ACM Transactions on Knowledge Discovery from Data, 5(2):1--36, Association for Computing Machinery (ACM), feb, 2011.
Fast Algorithms for Approximating the Singular Value Decomposition [link]Paper  doi  bibtex   
Link Prediction via Matrix Factorization. Menon, A. K. and Elkan, C. In Machine Learning and Knowledge Discovery in Databases, pages 437--452. Springer Berlin Heidelberg, 2011.
Link Prediction via Matrix Factorization [link]Paper  doi  bibtex   
2010 (2)
Dual Decomposition for Parsing with Non-Projective Head Automata. Koo, T.; Rush, A. M.; Collins, M.; Jaakkola, T.; and Sontag, D. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1288-1298, 2010.
Dual Decomposition for Parsing with Non-Projective Head Automata [pdf]Paper  abstract   bibtex   
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing. Rush, A. M.; Sontag, D.; Collins, M.; and Jaakkola, T. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1-11, 2010.
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing [pdf]Paper  abstract   bibtex   
2009 (5)
Improving propensity score weighting using machine learning. Lee, B., K.; Lessler, J.; and Stuart, E., A. Statistics in Medicine, 29(3):n/a-n/a, Wiley-Blackwell, 2009.
Improving propensity score weighting using machine learning [pdf]Paper  Improving propensity score weighting using machine learning [link]Website  abstract   bibtex   
ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes. Kim, Y.; Schmid, T.; Charbiwala, Z. M.; and Srivastava, M. B. In Proceedings of the 11th International Conference on Ubiquitous Computing, of Ubicomp '09, pages 245--254, New York, NY, USA, 2009. ACM.
ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes [link]Paper  doi  abstract   bibtex   
Clusters and Coarse Partitions in LP Relaxations. Sontag, D.; Globerson, A.; and Jaakkola, T. In Koller, D.; Schuurmans, D.; Bengio, Y.; and Bottou, L., editors, Advances in Neural Information Processing Systems 21, pages 1537–1544, 2009. MIT Press.
Clusters and Coarse Partitions in LP Relaxations [pdf]Paper  abstract   bibtex   
AG-ART: An adaptive approach to evolving ART architectures. Kaylani, A.; Georgiopoulos, M.; Mollaghasemi, M.; and Anagnostopoulos, G. C. Neurocomputing, 72(10–12):2079 - 2092, June, 2009. Lattice Computing and Natural Computing (JCIS 2007) / Neural Networks in Intelligent Systems Designn (ISDA 2007)
doi  abstract   bibtex   
Reforestation planning using Bayesian networks. Ordóñez Galán, C.; Matías, J.; Rivas, T.; and Bastante, F. Environmental Modelling & Software, 24(11):1285-1292, 11, 2009.
Reforestation planning using Bayesian networks [link]Website  abstract   bibtex   
2008 (1)
3D Bicipital Groove Shape Analysis and Relationship to Tendopathy. Ward, A.; Hamarneh, G.; and Schweitzer, M. Journal of Digital Imaging, 21(2):219-234, 2008.
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2007 (3)
Genetic Optimization of Art Neural Network Architectures. Kaylani, A.; Georgiopoulos, M.; Mollaghasemi, M.; and Anagnostopoulos, G. C. In Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing, of ASC '07, pages 225–230, Anaheim, CA, USA, 2007. ACTA Press.
Genetic Optimization of Art Neural Network Architectures [link]Paper  bibtex   
Anatomical Shape Analysis: Exploring the Relationship between Shape and Pathology. Ward, A.; Hamarneh, G.; and Schweitzer, M. In CIHR National Research Poster Competition, Canadian Student Health Research Forum (CSHRF), Winnipeg, June 6-7, 2007.
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Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. Knowles, D. A. and Ghahramani, Z. In 7th International Conference on Independent Component Analysis and Signal Separation (ICA), 2007.
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis [link]Paper  Infinite Sparse Factor Analysis and Infinite Independent Components Analysis [pdf]Pdf  doi  abstract   bibtex   
2006 (1)
3D Shape Analysis of the Supraspinatus Muscle. Ward, A.; Hamarneh, G.; Ashry, R.; and Schweitzer, M. In Medical Image Computing and Computer-Assisted Intervention Joint Diseases Workshop (MICCAI JD), pages 96-103, 2006.
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2005 (2)
Approximate Inference for Infinite Contingent Bayesian Networks. Milch, B.; Marthi, B.; Sontag, D.; Russell, S.; Ong, D. L.; and Kolobov, A. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 238–245, 2005.
Approximate Inference for Infinite Contingent Bayesian Networks [pdf]Paper  abstract   bibtex   1 download  
A Tutorial on the Cross-Entropy Method. Boer, P., D., E. 2005.
A Tutorial on the Cross-Entropy Method [pdf]Paper  bibtex   
2004 (1)
A Combined Transmembrane Topology and Signal Peptide Prediction Method. Käll, L.; Krogh, A.; and Sonnhammer, E. L. L Journal of Molecular Biology, 338(5):1027–1036, May, 2004.
A Combined Transmembrane Topology and Signal Peptide Prediction Method [link]Paper  doi  abstract   bibtex   
2002 (2)
Machine Learning in Automated Text Categorization. Sebastiani, F. ACM Comput Surv, 34(1):1–47, ACM, New York, NY, USA, 2002.
doi  abstract   bibtex   
Research abstract for semantic anomaly detection in dynamic data feeds with incomplete specifications. Raz, O. In Proceedings of the 24rd International Conference on Software Engineering, 2002. ICSE 2002, pages 733--734, May, 2002.
abstract   bibtex   
2000 (1)
Feature Subset Selection by Bayesian network-based optimization. Inza, I.; Larrañaga, P.; Etxeberria, R.; and Sierra, B. Artificial Intelligence, 123(1-2):157-184, 10, 2000.
Feature Subset Selection by Bayesian network-based optimization [link]Website  abstract   bibtex   
1999 (2)
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An ignorant belief network to forecast glucose concentration from clinical databases. Ramoni, M.; Riva, A.; Stefanelli, M.; and Patel, V. Artificial Intelligence in Medicine, 7(6):541-559, 12, 1995.
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On a learnability question associated to neural networks with continuous activations (extended abstract). DasGupta, B.; Siegelmann, H. T.; and Sontag, E. In COLT '94: Proceedings of the seventh annual conference on Computational learning theory, pages 47–56, New York, NY, USA, 1994. ACM Press.
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Fundamentals of Machine Learning for Neural Machine Translation. Kelleher, J. In Translating Europe Forum 2016: Focusing on Translation Technologies, Brussels, Belgium. 27-27 October, 2016.
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