Eureka

Researchers Develop EaPU Method for Memristor-Based AI Training

Researchers in China have developed an error-aware probabilistic update (EaPU) method that dramatically improves the ...
Tech Xplore

New memristor training method slashes AI energy use by six orders of magnitude

In a Nature Communications study, researchers from China have developed an error-aware probabilistic update (EaPU) method that aligns memristor hardware's noisy updates with neural network training, ...
blockchain

Celebrating Geoffrey Hinton: AI Pioneer’s Impact on Deep Learning and Neural Networks

According to Jeff Dean on Twitter, Geoffrey Hinton, often referred to as the 'Godfather of AI,' celebrates his birthday today. Hinton's pioneering research in neural networks and deep learning has ...
Forbes

This Team Is Rethinking AI’s Core: Perforated AI Bets On Dendrites

For more than eighty years, deep learning has relied on a simplified model of brain function. The 1943 McCulloch-Pitts model of the neuron fueled breakthroughs in image recognition, speech synthesis ...
Scientific Research Publishing

Magoulas, G.D., Vrahatis, M.N. and Androulakis, G.S. (1999) Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods. Neural ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...
IEEE

SIMBa: System Identification Methods Leveraging Backpropagation

Abstract: This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning ...
GitHub

An Alternative Approach: Recursive Backpropagation Without Topological Sorting

Very intuitive and simple, @karpathy! I was coding in parallel while watching your video and took a slightly different approach. For computing gradients of + and *, I used the fundamental derivative ...