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Offline data from outdated environments can still yield optimal policies when integrated correctly, as shown by our new hybrid reinforcement learning framework.
By decoupling trend and residual information, PMDformer achieves unprecedented accuracy in long-term time series forecasting, setting a new benchmark in the field.
CANS slashes inference latency by up to 50% in collaborative edge computing by enabling devices to learn optimal DNN partitions through shared feedback.
Untangling multilayer networks just got easier: T-GINEE uses tensors to explicitly model cross-layer dependencies, outperforming methods that treat layers independently.
