As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...

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: This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with ...

Computational power has become a critical factor in pushing the boundaries of what’s possible in machine learning. As models grow more complex and datasets expand exponentially, traditional CPU-based ...

The success of deep learning contrasts with its limited understanding. One example is stochastic gradient descent, the main algorithm used to train neural networks. It depends on hyperparameters whose ...

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 ...

The chief technology officer of a robotics startup told me earlier this year, “We thought we’d have to do a lot of work to build ‘ChatGPT for robotics.’ Instead, it turns out that, in a lot of cases, ...