lcayton.com

Lawrence Cayton

Work circle-a lcayton.com. I currently work as a machine learning engineer and scientist in the Seattle area. I was formerly a research scientist in the Empirical Inference Department. Of the Max Planck Institute for Intelligent Systems. Formerly Biological Cybernetics). I did my PhD at the University of California, San Diego, where I was advised by Sanjoy Dasgupta. For fast nearest neighbor search on a GPU or multicore CPU. In summer 2016, I taught a course on Applied Machine Learning.

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Lawrence Cayton | lcayton.com Reviews

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Work circle-a lcayton.com. I currently work as a machine learning engineer and scientist in the Seattle area. I was formerly a research scientist in the Empirical Inference Department. Of the Max Planck Institute for Intelligent Systems. Formerly Biological Cybernetics). I did my PhD at the University of California, San Diego, where I was advised by Sanjoy Dasgupta. For fast nearest neighbor search on a GPU or multicore CPU. In summer 2016, I taught a course on Applied Machine Learning.

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Lawrence Cayton | lcayton.com Reviews

https://lcayton.com

Work circle-a lcayton.com. I currently work as a machine learning engineer and scientist in the Seattle area. I was formerly a research scientist in the Empirical Inference Department. Of the Max Planck Institute for Intelligent Systems. Formerly Biological Cybernetics). I did my PhD at the University of California, San Diego, where I was advised by Sanjoy Dasgupta. For fast nearest neighbor search on a GPU or multicore CPU. In summer 2016, I taught a course on Applied Machine Learning.

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lcayton.com

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Lawrence Cayton: code

http://www.lcayton.com/code.html

Lawrence Cayton : code. All of my code is available on github. Random ball cover (RBC) - GPU. Version 0.2.6. Oct 2011. (Original release: August 2010). The RBC is a simple geometric data structure for fast nearest neighbor search in metric spaces that is provably sensitive to intrinsic. Dimensionality. Because of its design, the data structure can make use of the power of parallel systems, like GPUs. The slides. From the ADMS talk provide a simple overview of the data structure; see my papers. This is a ...

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Lawrence Cayton

http://www.lcayton.com/index.html

Work circle-a lcayton.com. I currently work as a machine learning engineer and scientist in the Seattle area. I was formerly a research scientist in the Empirical Inference Department. Of the Max Planck Institute for Intelligent Systems. Formerly Biological Cybernetics). I did my PhD at the University of California, San Diego, where I was advised by Sanjoy Dasgupta. For fast nearest neighbor search on a GPU or multicore CPU. In summer 2016, I taught a course on Applied Machine Learning.

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Lawrence Cayton

http://www.lcayton.com/papers.html

Lawrence Cayton : papers. L Cayton. Accelerating nearest neighbor search on manycore systems. Twenty-Sixth IEEE International Parallel and Distributed Processing Symposium (IPDPS). See ADMS paper for GPU implementation details). T Kam-Thong, C-A. Azencott, L. Cayton, B. Pütz, A. Altmann, N. Karbalai, P. Sämann, B. Schölkopf, B. Müller-Myhsok, and K. Borgwardt. GLIDE: GPU-based linear regression for the detection of epistasis. Human Heredity, 2012, 73:220 236. L Cayton. Efficient bregman range search.

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lcayton.com

Lawrence Cayton

Work circle-a lcayton.com. I currently work as a machine learning engineer and scientist in the Seattle area. I was formerly a research scientist in the Empirical Inference Department. Of the Max Planck Institute for Intelligent Systems. Formerly Biological Cybernetics). I did my PhD at the University of California, San Diego, where I was advised by Sanjoy Dasgupta. For fast nearest neighbor search on a GPU or multicore CPU. In summer 2016, I taught a course on Applied Machine Learning.

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