Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

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: Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several ...

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 ...

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, ...

Federated learning enables collaborative model training by aggregating gradients from multiple clients, thus preserving their private data. However, gradient inversion attacks can compromise this ...

This study provides a computable, direct, and mathematically rigorous approximation to the differential geometry of class manifolds for high-dimensional data, along with non-linear projections from ...

Add a description, image, and links to the gradient-algorithm topic page so that developers can more easily learn about it.