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Displaying 1-16 out of 16 total
Automatic Domain Identification for Linked Open Data
Found in: 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
By Sarasi Lalithsena,Pascal Hitzler,Amit Sheth,Prateek Jain
Issue Date:November 2013
pp. 205-212
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still c...
 
Improved Multiple Sequence Alignments Using Coupled Pattern Mining
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By K.S.M.Tozammel Hossain,Debprakash Patnaik,Srivatsan Laxman,Prateek Jain,Chris Bailey-Kellogg,Naren Ramakrishnan
Issue Date:September 2013
pp. 1098-1112
We present alignment refinement by mining coupled residues (ARMiCoRe), a novel approach to a classical bioinformatics problem, viz., multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in el...
 
Far-sighted active learning on a budget for image and video recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Sudheendra Vijayanarasimhan, Prateek Jain, Kristen Grauman
Issue Date:June 2010
pp. 3035-3042
Active learning methods aim to select the most informative unlabeled instances to label first, and can help to focus image or video annotations on the examples that will most improve a recognition system. However, most existing methods only make myopic que...
 
Fast Similarity Search for Learned Metrics
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Brian Kulis, Prateek Jain, Kristen Grauman
Issue Date:December 2009
pp. 2143-2157
We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships ...
 
Fast image search for learned metrics
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Prateek Jain, Brian Kulis, Kristen Grauman
Issue Date:June 2008
pp. 1-8
We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the images’ underlying relationships well. To ...
 
Integrating Stateful Services in Workflow
Found in: Asia-Pacific Software Engineering Conference
By Zakir Laliwala, Vikram Sorathia, Sanjay Chaudhary, Prateek Jain
Issue Date:December 2006
pp. 131-138
Long running business processes require composition of services for task accomplishment. BPEL provide a mechanism to model business workflow among collaborating Web Services distributed across organizations. For effective execution and consistent outcomes ...
 
Semantic based Service-Oriented Grid Architecture for Business Processes
Found in: Services Computing, IEEE International Conference on
By Zakir Laliwala, Prateek Jain, Sanjay Chaudhary
Issue Date:September 2006
pp. 423-430
B2B and long running B2C process is a complex business process that contains a set of services, state and transaction management, and involves notification of various events occurring during the execution of a process. Business processes are driven by even...
 
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Sudheendra Vijayanarasimhan,Prateek Jain,Kristen Grauman
Issue Date:February 2014
pp. 276-288
We consider the problem of retrieving the database points nearest to a given hyperplane query without exhaustively scanning the entire database. For this problem, we propose two hashing-based solutions. Our first approach maps the data to 2-bit binary keys...
 
Improved Multiple Sequence Alignments Using Coupled Pattern Mining
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Chris Bailey-Kellogg, Debprakash Patnaik, K. S. M. Tozammel Hossain, Naren Ramakrishnan, Prateek Jain, Srivatsan Laxman
Issue Date:September 2013
pp. 1098-1112
We present alignment refinement by mining coupled residues (ARMiCoRe), a novel approach to a classical bioinformatics problem, viz., multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in el...
     
A statistical and schema independent approach to identify equivalent properties on linked data
Found in: Proceedings of the 9th International Conference on Semantic Systems (I-SEMANTICS '13)
By Amit Sheth, Kalpa Gunaratna, Krishnaprasad Thirunarayan, Prateek Jain, Sanjaya Wijeratne
Issue Date:September 2013
pp. 33-40
Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community recently. Currently it consists of approximately 295 interlinked datasets with over 50 billion triples including 500 million links, and continues to expand in size....
     
Low-rank matrix completion using alternating minimization
Found in: Proceedings of the 45th annual ACM symposium on Symposium on theory of computing (STOC '13)
By Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Issue Date:June 2013
pp. 665-674
Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the mos...
     
Moving beyond SameAs with PLATO: partonomy detection for linked data
Found in: Proceedings of the 23rd ACM conference on Hypertext and social media (HT '12)
By Amit P. Sheth, Kunal Verma, Pascal Hitzler, Peter Z. Yeh, Prateek Jain
Issue Date:June 2012
pp. 33-42
The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of...
     
Geometry-aware metric learning
Found in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09)
By Inderjit S. Dhillon, Prateek Jain, Zhengdong Lu
Issue Date:June 2009
pp. 1-8
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pair-wise (dis-)similarity constraints, we learn a kernel matrix over the data that respects the provided side-information as well as the local geometry of the data....
     
Enhancing process-adaptation capabilities with web-based corporate radar technologies
Found in: Proceedings of the first international workshop on Ontology-supported business intelligence (OBI '08)
By Alex Kass, Amit Sheth, Kunal Verma, Peter Z. Yeh, Prateek Jain
Issue Date:October 2008
pp. 1-6
Dynamic business processes are capable of adapting themselves to internal events but lack the ability to adapt to external events. Corporate radars are capable of mining the Web for external events of interest to produce structured representations of these...
     
Rank minimization via online learning
Found in: Proceedings of the 25th international conference on Machine learning (ICML '08)
By Constantine Caramanis, Inderjit S. Dhillon, Prateek Jain, Raghu Meka
Issue Date:July 2008
pp. 656-663
Minimum rank problems arise frequently in machine learning applications and are notoriously difficult to solve due to the non-convex nature of the rank objective. In this paper, we present the first online learning approach for the problem of rank minimiza...
     
Program partitioning: a framework for combining static and dynamic analysis
Found in: Proceedings of the 2006 international workshop on Dynamic systems analysis (WODA '06)
By Pankaj Jalote, Prateek Jain, Taranbir Singh, Vipindeep Vangala
Issue Date:May 2006
pp. 11-16
For higher quality software, static analysis and dynamic analysis should be used in a complementary manner. In this work, we explore the concept of partitioning a program such that the partitions can be analyzed separately. With such partitioning, potentia...
     
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