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Exploring Time Representation in Transformers for Enhanced Learning
This chapter delves into the significance of including absolute time in transformers alongside relative positions for more precise learning. The discussion covers topics like prospective learning, system activation for optimal performance, and the impact of time on learning processes and system performance. It also examines the concept of time in daily resets, different time frames, and its role in understanding spatial-temporal dependencies for improved predictive purposes.