Loss functions and constraints improve sea surface height prediction

In order to understand currents, tides and other ocean dynamics, scientists need to accurately capture sea surface height, or ...
Learn With Jay on MSN

Mini-batch gradient descent in deep learning explained

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

Linear regression using gradient descent explained simply

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

A differentiable model for optimizing the genetic drivers of synaptogenesis

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

Deterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation

Abstract: Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed ...
IEEE

Accelerating Convergence of Stein Variational Gradient Descent via Deep Unfolding

Abstract: Stein variational gradient descent (SVGD) is a prominent particle-based variational inference method used for sampling a target distribution. In this paper, we propose two novel trainable ...
GitHub

Descent 3: Piccu Engine

This is a new engine for Descent 3, from the initial 1.5 source code release. It incorporates almost every change from InjectD3, among many more. At the moment, this includes the following features: ...