Temporal Distance

Temporal distance, the interval between events or states, is crucial for various applications, from time series forecasting to reinforcement learning and emotion recognition. Current research focuses on developing methods to accurately represent and utilize temporal distances, particularly in complex, stochastic systems, employing techniques like contrastive learning and attention mechanisms within novel architectures such as transformers. This work aims to improve the accuracy and efficiency of algorithms that rely on temporal information, impacting fields ranging from predictive modeling to the development of more robust and adaptable AI systems.