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» Embedding Heterogeneous Data Using Statistical Models
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98
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ICML
2010
IEEE
15 years 1 months ago
Local Minima Embedding
Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Minyoung Kim, Fernando De la Torre
PERCOM
2010
ACM
14 years 11 months ago
Sensor.Network: An open data exchange for the web of things
Abstract—Tiny, wireless, sensors embedded in a large number of Internet-capable devices–smart phones, cameras, cars, toys, medical instruments, home appliances and energy meter...
Vipul Gupta, Arshan Poursohi, Poornaprajna Udupi
104
Voted
BMCBI
2008
128views more  BMCBI 2008»
15 years 17 days ago
HAPSIMU: a genetic simulation platform for population-based association studies
Background: Population structure is an important cause leading to inconsistent results in population-based association studies (PBAS) of human diseases. Various statistical method...
Feng Zhang, Jianfeng Liu, Jie Chen, Hong-Wen Deng
86
Voted
ICML
2010
IEEE
15 years 1 months ago
Deep Supervised t-Distributed Embedding
Deep learning has been successfully applied to perform non-linear embedding. In this paper, we present supervised embedding techniques that use a deep network to collapse classes....
Martin Renqiang Min, Laurens van der Maaten, Zinen...
97
Voted
CORR
2007
Springer
140views Education» more  CORR 2007»
15 years 13 days ago
From the entropy to the statistical structure of spike trains
— We use statistical estimates of the entropy rate of spike train data in order to make inferences about the underlying structure of the spike train itself. We first examine a n...
Yun Gao, Ioannis Kontoyiannis, Elie Bienenstock