Abstract: Recently, deep learning methods have achieved great success in the intelligent fault diagnosis of rotating machinery. Of these, one-dimensional deep convolutional neural networks (1D-DCNNs) ...

Visual Attention Networks (VANs) leveraging Large Kernel Attention (LKA) have demonstrated remarkable performance in diverse computer vision tasks, often outperforming Vision Transformers (ViTs) in ...

Classical convolutional neural networks (CNNs) have achieved notable success in image classification but face challenges in scalability, interpretability, and computational cost. With the growing ...

Do you have ACL Group convolution kernel for NEON backend ? Can you help with the user kernel inclusion incase to be followed. Existing Convolution 2d num_groups is declared as not used.

I wrote a small benchmark comparing a 1D depthwise convolution implemented in PyTorch vs. pure JAX on GPU, and found that the JAX version is both substantially slower (≈3×) and draws noticeably more ...

Abstract: In computer vision, 2D convolution is arguably the most important operation performed by a ConvNet. Unsurprisingly, it has been the focus of intense software and hardware optimization and ...

Geoscientific visualization has become a prominent aspect of research result representation and a tool that enables researchers to enhance their knowledge in the course of their research investigation ...

Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and width (number of ...