arXiv:2603. 02234v3 Announce Type: replace-cross Abstract: The Strong Lottery Ticket Hypothesis (SLTH) states that large, randomly initialized neural networks contain sparse subnetworks capable of approximating a target function at initialization without training, suggesting that pruning alone is sufficient.
By Davide Ferre' (CNRS, COATI, UniCA, I3S), Fr\'ed\'eric Giroire (I3S, COATI, UniCA), Frederik Mallmann-Trenn (CNRS, COATI, I3S, UniCA), Emanuele Natale (CNRS, COATI, I3S, UniCA)
arXiv:2607. 20555v1 Announce Type: new Abstract: The lottery ticket hypothesis proposes that large random neural networks contain sparse subnetworks that can match the performance of dense models after comparable training.
By Bryce A. Christopherson, Jack Baretz, Darian Colgrove, Salah Dandan
The paper investigates the generalization behavior of the OPTQ quantization algorithm and its stochastic variant. It derives bounds on the expected squared error when a test point is drawn from a fixed distribution, linking this error to the calibration dataset and to the regularization parameter λ. The authors use these theoretical insights to propose a new recommendation for choosing λ, which shows improved performance in experiments compared to previous suggestions.
By Erin George, Rayan Saab
arXiv:2505. 18113v2 Announce Type: replace Abstract: Training quantized neural networks requires addressing the non-differentiable and discrete nature of the underlying optimization problem.
By Halyun Jeong, Jack Xin, Penghang Yin
The paper investigates how sparsity impacts the expressivity of graph neural networks, focusing on relational and temporal variants. It extends the Strong Expressive Lottery Ticket Hypothesis to multi-relational and temporal domains, proving that sufficiently large RGNNs contain sparse subnetworks that preserve 1‑RWL expressivity and providing a probabilistic bound for random pruning. Experiments validate the theoretical bounds, compare them to empirical results on synthetic data, and explore the relationship between pre‑training expressivity, optimization behavior, and prediction quality on temporal and molecular benchmarks.
arXiv:2510.08999v2 Announce Type: replace-cross
Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight...
By Ziyi Wang, Nan Jiang, Guang Lin, Qifan Song