Dataset-Dependent Effects of Cross-Depth Aggregation and Soft-Routed Experts in EEG Foundation Model Fine-Tuning
cs.LG
Submitted: 2026-09-15
Updated: 2026-09-15
Comments: 5 pages, 2 figures, 3 tables. Submitted to IEEE ICASSP 2027
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- BrainGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training
- TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning
- Attention Residuals
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks