Integrated silicon photonics has sparked a significant ramp-up of investment in both
academia and industry as a scalable, power-efficient, and eco-friendly solution. At the heart …

In the modern era, deep learning techniques have emerged as powerful tools in image
recognition. Convolutional Neural Networks, one of the deep learning tools, have attained …

We present a simple but powerful architecture of convolutional neural network, which has a
VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …

Brain–computer interfaces (BCIs) can restore communication to people who have lost the
ability to move or speak. So far, a major focus of BCI research has been on restoring gross …

Over the last few years, several Convolutional Neural Networks for object detection have
been proposed, characterised by different accuracy and speed. In viticulture, yield …

Low-level details and high-level semantics are both essential to the semantic segmentation
task. However, to speed up the model inference, current approaches almost always sacrifice …

Deep learning frameworks have often focused on either usability or speed, but not both.
PyTorch is a machine learning library that shows that these two goals are in fact compatible …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …