Bottleneck Analysis

Bottleneck analysis focuses on identifying and addressing performance limitations in complex systems, aiming to optimize efficiency and resource allocation. Current research emphasizes the use of autoencoders, recursive neural networks, and various bandit algorithms to detect and mitigate bottlenecks in diverse applications, including time series anomaly detection, object recognition in challenging conditions, and feature selection. These analyses are crucial for improving the performance of machine learning models, optimizing hardware utilization in resource-constrained environments like autonomous vehicles, and enhancing the interpretability of complex systems.