Large scale integration of renewable energy system with classical electrical power
generation system requires a precise balance to maintain and optimize the supply–demand …

In recent years, convolutional neural networks have been studied in the Fourier domain for a
limited environment, where competitive results can be expected for conventional image …

Purpose This paper proposes a novel self‐supervised learning framework that uses model
reinforcement, REference‐free LAtent map eXtraction with MOdel REinforcement (RELAX …

Conventional convolutional neural networks (CNNs) present a high computational workload
and memory access cost (CMC). Spectral domain CNNs (SpCNNs) offer a computationally …

Researchers have proposed various structured Convolutional Neural Networks (CNNs)
pruning strategies to make them work efficiently on edge devices. However, most of them …

The convolutional neural network (CNN) has gained widespread adoption in computer
vision (CV) applications in recent years. However, the high computational complexity of …

Conventional convolutional neural networks (CNNs), which are realized in the spatial
domain, present a high computational workload and memory access cost (CMC). Spectral …

Nous présentons un nouveau modèle reliant les réseaux de neurones convolutifs (CNNs) à
la vision biologique et à la physique fondamentale des particules. La propagation de …

Abstract For many popular Convolutional Neural Networks (CNNs), memory has become
one of the major constraints for their efficient training and inference on edge devices …