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Pruning at Initialisation through the lens of Graphon Limit: Convergence, Expressivity, and Generalisation
Hoang Pham, The-Anh Ta, Long Tran-Thanh
Pruning at Initialisation methods discover sparse, trainable subnetworks before training, but their theoretical mechanisms remain elusive. Existing analyses are often limited to fi…
The Graphon Limit Hypothesis: Understanding Neural Network Pruning via Infinite Width Analysis
Hoang Pham, The-Anh Ta, Tom Jacobs +2
Sparse neural networks promise efficiency, yet training them effectively remains a fundamental challenge. Despite advances in pruning methods that create sparse architectures, unde…
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…