Temporal Encoding

Temporal encoding, the process of representing temporal information within neural networks, aims to improve the processing and understanding of time-dependent data. Current research focuses on enhancing existing architectures like Transformers and Spiking Neural Networks (SNNs) through novel encoding methods, including hybrid approaches and the incorporation of time-step information, to better capture temporal dependencies in various data types such as time series, video, and point cloud sequences. These advancements are significant for improving the performance and energy efficiency of machine learning models in diverse applications, ranging from action recognition and gesture recognition to graph reasoning and neuromorphic computing.