SMA-DP-SGD augments DP-SGD with a spectral memory-aware fractional branch from prior privatized updates to improve accuracy on CIFAR and MNIST while preserving conditional differential privacy.
Motor imagery and direct brain- computer communication.Proceedings of the IEEE, 2001
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Gradient alignment persists throughout multi-step distillation training and causally drives unintended teacher trait acquisition in the student, while liminal training attenuates alignment but does not stop the acquisition.
SwitchBraidNet is a compact dual-path EEG classifier achieving 69.49% MI accuracy (FP16), 93.48% SSVEP accuracy (FP32), 64.82 bits/min hybrid ITR (FP16), and 3.03 KB INT8 size via quantization-aware training on OpenBMI.
Standard NLP classifiers can surface valid injury precursors from raw construction safety reports.
citing papers explorer
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SMA-DP: Spectral Memory-Aware Differential Privacy for Deep Learning
SMA-DP-SGD augments DP-SGD with a spectral memory-aware fractional branch from prior privatized updates to improve accuracy on CIFAR and MNIST while preserving conditional differential privacy.
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Sustained Gradient Alignment Mediates Subliminal Learning in a Multi-Step Setting: Evidence from MNIST Auxiliary Logit Distillation Experiment
Gradient alignment persists throughout multi-step distillation training and causally drives unintended teacher trait acquisition in the student, while liminal training attenuates alignment but does not stop the acquisition.
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SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
SwitchBraidNet is a compact dual-path EEG classifier achieving 69.49% MI accuracy (FP16), 93.48% SSVEP accuracy (FP32), 64.82 bits/min hybrid ITR (FP16), and 3.03 KB INT8 size via quantization-aware training on OpenBMI.