# Towards strong pruning for lottery tickets with non-zero biases

@article{Fischer2021TowardsSP, title={Towards strong pruning for lottery tickets with non-zero biases}, author={Jonas Fischer and Rebekka Burkholz}, journal={ArXiv}, year={2021}, volume={abs/2110.11150} }

The strong lottery ticket hypothesis holds the promise that pruning randomly initialized deep neural networks could offer a computationally efficient alternative to deep learning with stochastic gradient descent. Common parameter initialization schemes and existence proofs, however, are focused on networks with zero biases, thus foregoing the potential universal approximation property of pruning. To fill this gap, we extend multiple initialization schemes and existence proofs to non-zero biases…

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## 2 Citations

On the Existence of Universal Lottery Tickets

- Computer Science, MathematicsArXiv
- 2021

This work theoretically proves that not only do such universal tickets exist but they also do not require further training, and introduces a couple of technical innovations related to pruning for strong lottery tickets, including extensions of subset sum results and a strategy to leverage higher amounts of depth.

Plant 'n' Seek: Can You Find the Winning Ticket?

- Computer Science, MathematicsArXiv
- 2021

This work derives a framework to plant and hide target architectures within large randomly initialized neural networks and finds that current limitations of pruning algorithms to identify extremely sparse tickets are likely of algorithmic rather than fundamental nature.

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