GLT-PEFT combines Tucker decomposition for tensor low-rank adaptation with Lie group multiplicative updates and a gating mechanism to enable efficient cross-task transfer from segmentation pretraining to AD diagnosis in 3D CNNs.
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Frequency-adaptive tensor neural networks are proposed to overcome the frequency principle in TNNs for high-dimensional multi-scale problems by incorporating random Fourier features and 1D DFT on component functions.
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GLT-PEFT: Gated Lie-Tucker Parameter-Efficient Fine-Tuning for Alzheimer's Disease Diagnosis with Hippocampal Segmentation Pretraining
GLT-PEFT combines Tucker decomposition for tensor low-rank adaptation with Lie group multiplicative updates and a gating mechanism to enable efficient cross-task transfer from segmentation pretraining to AD diagnosis in 3D CNNs.
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Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems
Frequency-adaptive tensor neural networks are proposed to overcome the frequency principle in TNNs for high-dimensional multi-scale problems by incorporating random Fourier features and 1D DFT on component functions.