GitHub

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification

We analyze in a closed form the learning dynamics of stochastic gradient descent (SGD) for a single layer neural network classifying a high-dimensional Gaussian mixture where each cluster is assigned ...
JSTOR Daily

Stochastic integral representations and classification of sum- and max-infinitely divisible processes

Introduced is the notion of minimality for spectral representations of sum- and max-infinitely divisible processes and it is shown that the minimal spectral representation on a Borel space exists and ...
IEEE

Spintronic Stochastic Neuron-Based Deep Belief Networks for Image Classification

Abstract: Spintronic devices hold promise for AI, particularly in Boltzmann computing architectures, due to their tunable stochastic switching. We demonstrate a SOT-tuned spintronic neuron integrated ...
Semiconductor Engineering

Comparative Stochastic Process Variation Bands For N7, N5, And N3 At EUV

a) KLA-Tencor Corporation, Austin, TX 78759, U.S.A. b) KLA-Tencor Corporation, Milpitas, CA 95035, U.S.A. Stochastics effects are the ultimate limiter of optical lithography technology and are a major ...