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Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning
In this work, our prime objective is to study the phenomena of quantum chaos and complexity in the machine learning dynamics of Quantum Neural Network (QNN).
Geometry Perspective Of Estimating Learning Capability Of Neural Networks
The paper uses statistical and differential geometric motivation to acquire prior information about the learning capability of an artificial neural network on a given dataset.
Sewage Pooling Test for SARS-CoV-2
We propose a procedure to detect CoVID-19 affected localities using a sewage mass testing and pooling mechanism which has gained ground in recent times.
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