Phys.org

Physics of foam strangely resembles AI training

Foams are everywhere: soap suds, shaving cream, whipped toppings and food emulsions like mayonnaise. For decades, scientists believed that foams behave like glass, their microscopic components trapped ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using JavaScript

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 ...
IEEE

Unveiling Illusionary Robust Features: A Novel Approach for Adversarial Defenses in Deep Neural Networks

Abstract: Deep Neural Networks (DNNs) are vulnerable to visually imperceptible perturbations, known as Adversarial Examples (AEs). The leading hypothesis attributes this susceptibility to “non-robust ...
marktechpost

Understanding the Artificial Neural Networks ANNs

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Modeled after the human brain, ANNs enable machines to learn ...
Frontiers

Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks

Understanding cognitive processes in the brain demands sophisticated models capable of replicating neural dynamics at large scales. We present a physiologically inspired speech recognition ...
IEEE

An Efficient Inversion Framework for Audio-Magnetotellurics With Borehole Constraints Combining Supervised Descent Method and Gaussian Distribution Modeling Strategy

Abstract: In audio-magnetotellurics (AMT) inversion, the resistivity model derived from data is crucial for understanding geological properties. Current AMT inversion methods such as Gaussian–Newton ...
PNAS

On the different regimes of stochastic gradient descent

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 ...
GitHub

Inverting CNN (iCNN): image reconstruction from CNN features

Note: This package works with Python 2 and Caffe. Python 3 + PyTorch implementation of the reconstruction function is included in bdpy. Example code is available at brain-decoding-cookbook-public.