4 papers
Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability
Ambarish Moharil
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons (MLPs) require dynamic re-training within an optimization environment.…
Recursive Self-Similarity in Deep Weight Spaces of Neural Architectures: A Fractal and Coarse Geometry Perspective
Ambarish Moharil, Indika Kumara, Damian Andrew Tamburri +2
This paper conceptualizes the Deep Weight Spaces (DWS) of neural architectures as hierarchical, fractal-like, coarse geometric structures observable at discrete integer scales thro…
A Scale-Invariant Diagnostic Approach Towards Understanding Dynamics of Deep Neural Networks
Ambarish Moharil, Damian Tamburri, Indika Kumara +2
This paper introduces a scale-invariant methodology employing \textit{Fractal Geometry} to analyze and explain the nonlinear dynamics of complex connectionist systems. By leveragin…
To Reduce Gross NPA and Classify Defaulters Using Shannon Entropy
Ambarish Moharil, Nikhil Sonavane, Chirag Kedia +1
Non Performing Asset(NPA) has been in a serious attention by banks over the past few years. NPA cause a huge loss to the banks hence it becomes an extremely critical step in decidi…