mlsanityData to 7 Oct 2026

Not All Lotteries Are Made Equal

arXiv 2206.08175 ↗First posted 16 Jun 2022cs.LGcited by 0

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Abstract (arXiv)

The Lottery Ticket Hypothesis (LTH) states that for a reasonably sized neural network, a sub-network within the same network yields no less performance than the dense counterpart when trained from the same initialization. This work investigates the relation between model size and the ease of finding these sparse sub-networks. We show through experiments that, surprisingly, under a finite budget, smaller models benefit more from Ticket Search (TS).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 19 references that are in the map. First posted on arXiv 16 Jun 2022.

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18 of 19 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  6. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,250
  7. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
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  11. 2016On the Expressive Power of Deep Neural Networkscited by 302
  12. 2019Quantization Networkscited by 151
6 more
  1. 2015Structured Pruning of Deep Convolutional Neural Networkscited by 148
  2. 2015Fixed Point Quantization of Deep Convolutional Networkscited by 141
  3. 2020Layer-adaptive sparsity for the Magnitude-based Pruningcited by 122
  4. 2017Deep Learning with Low Precision by Half-wave Gaussian Quantizationcited by 115
  5. 2019Winning the Lottery with Continuous Sparsificationcited by 99
  6. 2019Stabilizing the Lottery Ticket Hypothesiscited by 36

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