Sequential Deep

Sequential deep learning focuses on leveraging the inherent sequential nature of data—like time series or text—to improve the performance of deep neural networks. Current research emphasizes efficient architectures such as State Space Models (SSMs) and recurrent networks (LSTMs, GRUs), often incorporating techniques like quantization for resource-constrained deployments and attention mechanisms for improved feature extraction. These advancements are driving progress in diverse applications, including phishing website detection, predictive maintenance (e.g., water pumps), and medical diagnosis (e.g., COVID-19 progression tracking), by enabling more accurate and efficient analysis of sequential data. Furthermore, investigations into the equivalence of deep and shallow architectures are exploring ways to improve training efficiency and reduce computational complexity.