1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Subspace-Constrained Federated Learning with Low-Rank Adaptation
Neranjan Senarath, Rohit Muralitharan, Sadia Asif
Federated low-rank adaptation methods are attractive for fine-tuning large models under communication and privacy constraints, but heterogeneous client data can induce geometric mi…
cs.CV2022★ 1 cited
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
Yasiru Ranasinghe, Kavinga Weerasooriya, Roshan Godaliyadda +6
In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a s…