5 citations · 21 across the 22 of their papers we have counts for
5 papers · 1 filter
Using Synthetic Data to estimate the True Error is theoretically and practically doable
Hai Hoang Thanh, Duy-Tung Nguyen, Hung The Tran +1
Accurately evaluating model performance is crucial for deploying machine learning systems in real-world applications. Traditional methods often require a sufficiently large labeled…
The Hidden Power of Normalization Layers in Neural Networks: Exponential Capacity Control
Khoat Than
Normalization layers are critical components of modern AI systems, such as ChatGPT, Gemini, DeepSeek, etc. Empirically, they are known to stabilize training dynamics and improve ge…
Provably Improving Generalization of Few-Shot Models with Synthetic Data
Lan-Cuong Nguyen, Quan Nguyen-Tri, Bang Tran Khanh +3
Few-shot image classification remains challenging due to the scarcity of labeled training examples. Augmenting them with synthetic data has emerged as a promising way to alleviate…
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
Kien Tran Duc Tuan, Tam Nguyen Trong, Son Nguyen Hoang +2
Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential. A key…
Non-vacuous Generalization Bounds for Deep Neural Networks without any modification to the trained models
Khoat Than, Dat Phan
Understanding and certifying the behavior of modern deep neural networks remains a fundamental challenge in reliable machine learning. We introduce a new class of data-dependent ge…