4 citations · 8 across the 5 of their papers we have counts for
6 papers
Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradients
Minh Duong Nguyen, Khanh Le, Khoi Do +4
In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergenc…
Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation
Edison Mucllari, Vasily Zadorozhnyy, Cole Pospisil +2
In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, partic…
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen +4
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning…
HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System
Bao-Sinh Nguyen, Quang-Bach Tran, Tuan-Anh Nguyen Dang +2
Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extra…
Deep Diffusion Processes for Active Learning of Hyperspectral Images
Abiy Tasissa, Duc Nguyen, James Murphy
A method for active learning of hyperspectral images (HSI) is proposed, which combines deep learning with diffusion processes on graphs. A deep variational autoencoder extracts smo…
Improving Long Handwritten Text Line Recognition with Convolutional Multi-way Associative Memory
Duc Nguyen, Nhan Tran, Hung Le
Convolutional Recurrent Neural Networks (CRNNs) excel at scene text recognition. Unfortunately, they are likely to suffer from vanishing/exploding gradient problems when processing…