This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient ...

ABSTRACT: In this paper, we consider a more general bi-level optimization problem, where the inner objective function is consisted of three convex functions, involving a smooth and two non-smooth ...

Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...

ABSTRACT: As drivers age, roadway conditions may become more challenging, particularly when normal aging is coupled with cognitive decline. Driving during lower visibility conditions, such as ...

Abstract: We introduce, for the first time in wireless communication networks, a quantum gradient descent (QGD) algorithm to maximize sum data rates in non-orthogonal multiple access (NOMA)-based ...

Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix ...

This week I interviewed Senator Amy Klobuchar, Democrat of Minnesota, about her Preventing Algorithmic Collusion Act. If you don’t know what algorithmic collusion is, it’s time to get educated, ...