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- research-articleNovember 2017
Sympiler: transforming sparse matrix codes by decoupling symbolic analysis
SC '17: Proceedings of the International Conference for High Performance Computing, Networking, Storage and AnalysisNovember 2017, Article No.: 13, Pages 1–13https://doi.org/10.1145/3126908.3126936Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse codes. The computation patterns in sparse numerical methods are guided by ...
- research-articleAugust 2015
Person Re-Identification by Iterative Re-Weighted Sparse Ranking
IEEE Transactions on Pattern Analysis and Machine Intelligence (ITPM), Volume 37, Issue 8Aug. 2015, Pages 1629–1642https://doi.org/10.1109/TPAMI.2014.2369055In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable ...
- ArticleDecember 2014
Multimodal Music and Lyrics Fusion Classifier for Artist Identification
ICMLA '14: Proceedings of the 2014 13th International Conference on Machine Learning and ApplicationsDecember 2014, Pages 506–509https://doi.org/10.1109/ICMLA.2014.88Humans interact with each other using different communication modalities including speech, gestures and written documents. In the absence of one modality or presence of a noisy modality, other modalities can benefit precision of systems. HCI systems can ...
- short-paperAugust 2013
Sparse selection of training data for touch correction systems
MobileHCI '13: Proceedings of the 15th international conference on Human-computer interaction with mobile devices and servicesAugust 2013, Pages 404–407https://doi.org/10.1145/2493190.2493241Touch offset models which improve input accuracy on mobile touch screen devices typically require the use of a large number of training points. In this paper, we describe a method for selecting training points such that high performance can be attained ...
- research-articleMarch 2013
Learning non-linear classifiers with a sparsity constraint using L1 regularization
SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied ComputingMarch 2013, Pages 167–169https://doi.org/10.1145/2480362.2480396When combined with kernels, Support Vector Machines (SVMs) often achieve outstanding accuracy. This comes, however, at the cost of very expensive predictions. To address this issue, we consider the problem of learning a non-linear classifier with a ...
- research-articleJanuary 2013
Regularized Latent Semantic Indexing: A New Approach to Large-Scale Topic Modeling
ACM Transactions on Information Systems (TOIS), Volume 31, Issue 1Article No.: 5, Pages 1–44https://doi.org/10.1145/2414782.2414787Topic modeling provides a powerful way to analyze the content of a collection of documents. It has become a popular tool in many research areas, such as text mining, information retrieval, natural language processing, and other related fields. In real-...
- ArticleSeptember 2012
Sparse linear wind farm energy forecast
ICANN'12: Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part IISeptember 2012, Pages 557–564https://doi.org/10.1007/978-3-642-33266-1_69In this work we will apply sparse linear regression methods to forecast wind farm energy production using numerical weather prediction (NWP) features over several pressure levels, a problem where pattern dimension can become very large. We shall place ...
- research-articleJuly 2011
Regularized latent semantic indexing
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information RetrievalJuly 2011, Pages 685–694https://doi.org/10.1145/2009916.2010008Topic modeling can boost the performance of information retrieval, but its real-world application is limited due to scalability issues. Scaling to larger document collections via parallelization is an active area of research, but most solutions require ...