Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.

Mini Batch Gradient Descent is an algorithm that helps to speed up learning while dealing with a large dataset. Instead of ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

Abstract: In this study, we consider the downlink beamforming problem in millimeter wave (mmWave) systems subjected to both path blockages and imperfect channel state information (CSI), and propose a ...

Abstract: This paper studies the feasibility of solving matrix equations in the method of moment (MoM) based on the stochastic gradient descent (SGD) technique (SGD-MoM). We adopted the optimization ...

DeepSeek has introduced Manifold-Constrained Hyper-Connections (mHC), a novel architecture that stabilizes AI training and ...

Stochastic modelling is the development of mathematical models for non-deterministic physical systems, which can adopt many possible behaviours starting from any given initial condition. Monte-Carlo ...

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

Color gradient filament is fun stuff to play with. It lets you make 3D prints that slowly fade from one color to another along the Z-axis. [David Gozzard] wanted to do some printing with this effect, ...

Pupil dilation provides a physiological readout of information gain during the brain's internal process of belief updating in the context of associative learning.

LLNL researchers break barriers in 3D nanofabrication - transforming two-photon lithography (TPL) into a wafer-scale ...