This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and ...

Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Opinion: Lidiya Mishchenko and Pooya Shoghi explain how to bridge a gap preventing successful patent claims to protect new ...

A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Using an algorithm they call the Krakencoder, researchers at Weill Cornell Medicine are a step closer to unraveling how the brain's wiring supports the way we think and act. The study, published June ...

d3 svg drawing threejs machine-learning deep-learning neural-network diagrams. Updated Nov 26, 2024; JavaScript; janhuenermann / neurojs. Star 4 ... 🚀 Blazing fast neuro-evolution & backpropagation ...