Abstract: Sparse coding, an unsupervised feature learning technique, is often used as a basic building block to construct deep networks. Convolutional sparse coding is proposed in the literature to ...

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the ...

Abstract: Bluetooth and Wi-Fi technology are commonly used in many areas, including smart phones, smart wearable devices, and Internet of Things (IoT). These technologies build communication bridges ...

The interplay between data symmetries and network architecture is key for efficient learning in neural networks. Convolutional neural networks perform well in image recognition by exploiting the ...

Rodrigo Caye Daudt, Bertrand Le Saux, Alexandre Boulch. (2018, October). Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP ...

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient.

The receptive field is defined as the region in the input space that a particular CNN’s feature is looking at (i.e. be affected by). For convolutional neural network, the number of output features in ...

The code in this repository implements an efficient generalization of the popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in our paper: Michaël Defferrard, Xavier Bresson, ...