American Institute of Physics

Hybrid convolutional neural network yields energy-efficient approach with analog first layer

Oscillatory retinal neuron networks don’t require external voltage sources and show comparable performance to cutting-edge GPU-based convolutional neural networks, for energy costs thousands of times ...
Nature

Training convolutional neural networks with the Forward–Forward Algorithm

Machine learning using deep neural networks (DNN) continues to transform human life in areas as different as art (DALL-E, stable diffusion), medicine (Alpha-Fold), transport, or natural language ...
Nature

A novel hybrid inception-xception convolutional neural network for efficient plant disease classification and detection

Plants are essential at all stages of living things. Plant pests, diseases, and symptoms are most regularly visible in plant leaves and fruits and sometimes within the roots. Yet, their diagnosis by ...

In 2012, Alex Krizhevsky trained AlexNet with GPUs, accelerating the rise of modern AI vision

In 2012, a groundbreaking computer program named AlexNet dramatically advanced machine vision. This system, utilizing ...