Most Downloaded

Published in July 2024   Title AI Empowered Wireless Communications: From Bits to Semantics Authors Z. Qin et al. Abstract Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be…

Published in September 2023   Title Training Spiking Neural Networks Using Lessons From Deep Learning Authors J. K. Eshraghian et al. Abstract The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and…

Published in November 1998   Authors Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner Abstract Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can…

Published in August 2019   Authors Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang Abstract With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant…

Published in March 2021   Authors W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller Abstract With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial…

Published in March 2023   Authors Z. Zou, K. Chen, Z. Shi, Y. Guo, and J. Ye Abstract Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Over the past…

Published in Jan 2021   Authors F. Zhuang et al. Abstract Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a…

Authors L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller Abstract Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data…