Artificial neural networks (ANNs), a popular path towards artificial intelligence, have
experienced remarkable success via mature models, various benchmarks, open-source …

Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit
information, which are not only biologically realistic but also suitable for ultralow-power …

Abstract Spiking Neural Networks (SNNs) are receiving increasing attention due to their
biological plausibility and the potential for ultra-low-power eventdriven neuromorphic …

Distracted driving is among the most important reasons for traffic accidents today. Recently,
there is an increasing interest in building driver assistance systems that detect the actions of …

The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power
consumption and powerful computing capability. However, the lack of effective learning …

Federated learning (FL) is a privacy-preserving technique for training a vast amount of
decentralized data and making inferences on mobile devices. As a typical language …

More recently, Bidirectional Encoder Representations from Transformers (BERT) was
proposed and has achieved impressive success on many natural language processing …

State-of-the-art neural network language models (NNLMs) represented by long short term
memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly …

Sugar price forecasting has attracted extensive attention from policymakers due to its
significant impact on people's daily lives and markets. In this paper, we present a novel …

State-of-the-art neural language models (LMs) represented by Transformers are highly
complex. Their use of fixed, deterministic parameter estimates fail to account for model …