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Muon's touted superiority in large-scale training may not hold up under controlled conditions, challenging its status as a go-to optimizer.
Adversarial training can effectively disentangle session-specific noise from task-relevant speech features in brain-computer interfaces, leading to more robust decoding across recording sessions.
