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Convolutional and Residual Networks Provably Contain Lottery Tickets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use (L+1) construction LT pruned from a random source network with ReLU activations (vs target) | 3-layer CNN on MNIST · MNIST | test accuracy (%): -0.08 vs target | No clear effect |
| Use (L+1) construction LT for ResNet-22 with ReLU | ResNet-22 on CIFAR-10 · CIFAR-10 | test accuracy (%): -0.36 vs target (all LT); best 50% LT exceeds target slightly | No clear effect |
| Use sigmoid activation in the (L+1) LT construction for ResNet-22 | ResNet-22 on CIFAR-10 · CIFAR-10 | test accuracy (%): -3.66 vs target (all LT); -0.5 for best 50% | Hurt |
| Select the best 50% of LT repetitions rather than all (ablation) | ResNet-22 LTs on CIFAR-10 · CIFAR-10 | test accuracy (%): +0.37 for ReLU, +0.49 for Leaky ReLU, +0.32 for Tanh, +4.2 for Sigmoid | Helped |
| Parameter count of LT vs target (ResNet-22, ReLU) | ResNet-22 LT (L+1) construction · CIFAR-10 | nonzero prunable parameters: 1058140 vs 31069 target | Hurt |
Closest papers by meaning
- Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks (ICLR 2022 · similarity 0.94)
- On the Existence of Universal Lottery Tickets (ICLR 2022 · similarity 0.94)
- Most Activation Functions Can Win the Lottery Without Excessive Depth (NeurIPS 2022 · similarity 0.93)
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (ICLR 2019 · similarity 0.92)
- Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets (NeurIPS 2023 · similarity 0.91)
- Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win (ICML 2025 · similarity 0.89)
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