EEG foundation models show no single winner across failure modes, attend to correct brain regions but decode corrupted signals, and retain task information in early layers while late layers adapt during fine-tuning.
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , year =
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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An explanatory book that supplies a clear mental map and intuition for how Vision-Language Models combine vision and language capabilities.
citing papers explorer
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Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
EEG foundation models show no single winner across failure modes, attend to correct brain regions but decode corrupted signals, and retain task information in early layers while late layers adapt during fine-tuning.
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From Pixels to Prompts: Vision-Language Models
An explanatory book that supplies a clear mental map and intuition for how Vision-Language Models combine vision and language capabilities.