Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Chandola, Varun"'
Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase in training time without significant improvement in the performance. T
Externí odkaz:
http://arxiv.org/abs/2303.00954
Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor. This poses limitations in use-case sce
Externí odkaz:
http://arxiv.org/abs/2211.14885
Complete computation of turbulent combustion flow involves two separate steps: mapping reaction kinetics to low-dimensional manifolds and looking-up this approximate manifold during CFD run-time to estimate the thermo-chemical state variables. In our
Externí odkaz:
http://arxiv.org/abs/2211.14098
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve
Externí odkaz:
http://arxiv.org/abs/2211.03022
Autor:
Sankhe, Pranav, Hall, Seventy F., Sage, Melanie, Rodriquez, Maria Y., Chandola, Varun, Joseph, Kenneth
Youth in the American foster care system are significantly more likely than their peers to face a number of negative life outcomes, from homelessness to incarceration. Administrative data on these youth have the potential to provide insights that can
Externí odkaz:
http://arxiv.org/abs/2208.01802
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve
Externí odkaz:
http://arxiv.org/abs/2202.09855
Publikováno v:
In Computational Materials Science September 2024 244
Most current anomaly detection methods suffer from the curse of dimensionality when dealing with high-dimensional data. We propose an anomaly detection algorithm that can scale to high-dimensional data using concepts from the theory of large deviatio
Externí odkaz:
http://arxiv.org/abs/2109.13698
Publikováno v:
ICMLA 2021
Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity. This paper describes experiments applying state-of-the-art object classifiers toward a unique set of im
Externí odkaz:
http://arxiv.org/abs/2109.12040
Publikováno v:
In Journal of Computational Science September 2023 72