Computer Science > Machine Learning
arXiv:2603.08914 (cs)
[Submitted on 9 Mar 2026]
Title:Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates
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Abstract:Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that randomly initialized networks contain sparse subnetworks achieving competitive accuracy without weight training. Existing SLT methods, notably edge-popup, rely on non-differentiable score-based selection, limiting optimization efficiency and scalability. We propose using continuously relaxed Bernoulli gates to discover SLTs through fully differentiable, end-to-end optimization - training only gating parameters while keeping all network weights frozen at their initialized values. Continuous relaxation enables direct gradient-based optimization of an $\ell_0$-regularization objective, eliminating the need for non-differentiable gradient estimators or iterative pruning cycles. To our knowledge, this is the first fully differentiable approach for SLT discovery that avoids straight-through estimator approximations. Experiments across fully connected networks, CNNs (ResNet, Wide-ResNet), and Vision Transformers (ViT, Swin-T) demonstrate up to 90% sparsity with minimal accuracy loss - nearly double the sparsity achieved by edge-popup at comparable accuracy - establishing a scalable framework for pre-training network sparsification.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.08914 [cs.LG] |
| (or arXiv:2603.08914v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08914
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View a PDF of the paper titled Uncovering a Winning Lottery Ticket with Continuously Relaxed Bernoulli Gates, by Itamar Tsayag and 1 other authors
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