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pro vyhledávání: '"Sinha, Kaushik"'
Autor:
Ram, Parikshit, Sinha, Kaushik
The mathematical formalization of a neurological mechanism in the olfactory circuit of a fruit-fly as a locality sensitive hash (Flyhash) and bloom filter (FBF) has been recently proposed and "reprogrammed" for various machine learning tasks such as
Externí odkaz:
http://arxiv.org/abs/2112.07157
Autor:
Sinha, Kaushik, Ram, Parikshit
Inspired by the fruit-fly olfactory circuit, the Fly Bloom Filter [Dasgupta et al., 2018] is able to efficiently summarize the data with a single pass and has been used for novelty detection. We propose a new classifier (for binary and multi-class cl
Externí odkaz:
http://arxiv.org/abs/2008.08685
Traditionally, there have been few options for navigational aids for the blind and visually impaired (BVI) in large indoor spaces. Some recent indoor navigation systems allow users equipped with smartphones to interact with low cost Bluetoothbased be
Externí odkaz:
http://arxiv.org/abs/1802.05735
Autor:
Sinha, Kaushik, de Weck, Olivier L.
The complexity of highly interconnected systems is rooted in the interwoven architecture defined by its connectivity structure. In this paper, we develop matrix energy of the underlying connectivity structure as a measure of topological complexity an
Externí odkaz:
http://arxiv.org/abs/1608.08456
Autor:
Bandyopadhyay, Dhrubajyoti, Banerjee, Upasana, Hajra, Adrija, Chakraborty, Sandipan, Amgai, Birendra, Ghosh, Raktim K., Haddadin, Faris I., Modi, Vivek A., Sinha, Kaushik, Aronow, Wilbert S., Deedwania, Prakash, Lavie, Carl J.
Publikováno v:
In Current Problems in Cardiology March 2021 46(3)
Autor:
Keivani, Omid, Sinha, Kaushik
Publikováno v:
In Information Sciences 6 February 2021 546:526-542
Autor:
Dasgupta, Sanjoy, Sinha, Kaushik
The k-d tree was one of the first spatial data structures proposed for nearest neighbor search. Its efficacy is diminished in high-dimensional spaces, but several variants, with randomization and overlapping cells, have proved to be successful in pra
Externí odkaz:
http://arxiv.org/abs/1302.1948
Principal components analysis (PCA) is a standard tool for identifying good low-dimensional approximations to data in high dimension. Many data sets of interest contain private or sensitive information about individuals. Algorithms which operate on s
Externí odkaz:
http://arxiv.org/abs/1207.2812
Autor:
Belkin, Mikhail, Sinha, Kaushik
The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and machine learning. However, despite major progress, the
Externí odkaz:
http://arxiv.org/abs/1004.4864
Autor:
Belkin, Mikhail, Sinha, Kaushik
In this paper we present a method for learning the parameters of a mixture of $k$ identical spherical Gaussians in $n$-dimensional space with an arbitrarily small separation between the components. Our algorithm is polynomial in all parameters other
Externí odkaz:
http://arxiv.org/abs/0907.1054