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of 184
pro vyhledávání: '"Banerjee, Arunava"'
Autor:
Chattopadhyay, Anik, Banerjee, Arunava
Sensory stimuli in animals are encoded into spike trains by neurons, offering advantages such as sparsity, energy efficiency, and high temporal resolution. This paper presents a signal processing framework that deterministically encodes continuous-ti
Externí odkaz:
http://arxiv.org/abs/2408.05950
Autor:
Lebedev, Yury, Banerjee, Arunava
Binomial trees are widely used in the financial sector for valuing securities with early exercise characteristics, such as American stock options. However, while effective in many scenarios, pricing options with CRR binomial trees are limited. Major
Externí odkaz:
http://arxiv.org/abs/2405.16333
Randomization has shown catalyzing effects in linear algebra with promising perspectives for tackling computational challenges in large-scale problems. For solving a system of linear equations, we demonstrate the convergence of a broad class of algor
Externí odkaz:
http://arxiv.org/abs/2111.06931
Autor:
Kummer, Michael, Banerjee, Arunava
The characterization of neural responses to sensory stimuli is a central problem in neuroscience. Spike-triggered average (STA), an influential technique, has been used to extract optimal linear kernels in a variety of animal subjects. However, when
Externí odkaz:
http://arxiv.org/abs/2005.05572
Akademický článek
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Directional data emerges in a wide array of applications, ranging from atmospheric sciences to medical imaging. Modeling such data, however, poses unique challenges by virtue of their being constrained to non-Euclidean spaces like manifolds. Here, we
Externí odkaz:
http://arxiv.org/abs/1907.04303
Autor:
Chattopadhyay, Anik, Banerjee, Arunava
In many animal sensory pathways, the transformation from external stimuli to spike trains is essentially deterministic. In this context, a new mathematical framework for coding and reconstruction, based on a biologically plausible model of the spikin
Externí odkaz:
http://arxiv.org/abs/1906.00092
Autor:
Sarkar, Rajasree, Amrr, Syed Muhammad, Bhutto, Javed Khan, Saidi, Abdelaziz Salah, Algethami, Abdullah, Banerjee, Arunava
Publikováno v:
In Acta Astronautica January 2023 202:130-138
Autor:
Choi, Inchul, Banerjee, Arunava
Despite recent advances, estimating optical flow remains a challenging problem in the presence of illumination change, large occlusions or fast movement. In this paper, we propose a novel optical flow estimation framework which can provide accurate d
Externí odkaz:
http://arxiv.org/abs/1804.03787
Analysis of a Bayesian mixture model for the Matrix Langevin distribution on the Stiefel manifold is presented. The model exploits a particular parametrization of the Matrix Langevin distribution, various aspects of which are elaborated on. A general
Externí odkaz:
http://arxiv.org/abs/1708.07196