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LoadKAN not only forecasts electricity demand with high accuracy but also deciphers complex relationships between mobility patterns and load, revealing insights that traditional black-box models obscure.
LLMs can now predict where drivers look with uncanny human-like accuracy, thanks to a new dataset and architecture that grounds attention in objects, not just scenes.
A single EEG decoding architecture, DSAINet, achieves state-of-the-art generalizability across diverse tasks and datasets without task-specific tuning, despite having only 77K parameters.
