4 papers
From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions
Agnideep Aich, Ashit Baran Aich, Bruce Wade
We study local linear convergence of gradient descent for finite-width feedforward networks under the squared empirical loss. Prior work shows that GD can remain confined to a Loca…
Bag of Coins: A Statistical Probe into Neural Confidence Structures
Agnideep Aich, Sameera Hewage, Md Monzur Murshed +2
Modern neural networks often produce miscalibrated confidence scores and struggle to detect out-of-distribution (OOD) inputs, while most existing methods post-process outputs witho…
Symplectic Generative Networks (SGNs): A Hamiltonian Framework for Invertible Deep Generative Modeling
Agnideep Aich, Ashit Aich
We introduce the \emph{Symplectic Generative Network (SGN)}, a deep generative model that leverages Hamiltonian mechanics to construct an invertible, volume-preserving mapping betw…
A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study
Agnideep Aich, Sameera Hewage, Md Monzur Murshed +5
In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing bot…