To enable real-time processing of communication algorithms, in this paper, we propose a new deep neural network (DNN) architecture for algorithm approximation. Based on the idea of deep unfolding, we ...

Training algorithm breaks barriers to deep physical neural networks. ScienceDaily . Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2023 / 12 / 231207161444.htm ...

Meanwhile, widely adopted deep neural network architectures, for example, ResNets or DenseNets, are manually crafted on benchmark datasets, which hamper their generalization ability to other domains.

EPFL researchers have developed a groundbreaking algorithm that efficiently trains analog neural networks, offering an energy-efficient alternative to traditional digital networks. This method, which ...

1. Introduction. Deep learning is achieving outstanding results in various machine learning tasks (He et al., 2015a; LeCun et al., 2015), but for applications that require real-time interaction with ...

In addition to pure deep neural networks (DNNs), sometimes people use hybrid vision models, which combine deep learning with classical machine learning algorithms that perform specific sub-tasks.

In neural networks, for a long time people have understood that there’s a relationship between iteration, recursion, and the current neural networks. In graph neural networks, the same sort of ...