HadamardNet applies Hadamard-coded outputs to segmentation and detection, with a novel projection-based decoder that supplies inconsistency measures for SOTA perturbation detection while preserving clean-data performance.
Multi-view self-supervised learning enhances automatic sleep staging from eeg signals
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Randomly initialized Transformers act as adaptive sequence smoothers for sleep staging via a Random Attention Prior Kernel, with gains mainly from inductive bias rather than training.
citing papers explorer
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Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation
HadamardNet applies Hadamard-coded outputs to segmentation and detection, with a novel projection-based decoder that supplies inconsistency measures for SOTA perturbation detection while preserving clean-data performance.
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Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging
Randomly initialized Transformers act as adaptive sequence smoothers for sleep staging via a Random Attention Prior Kernel, with gains mainly from inductive bias rather than training.