mlsanityData to 7 Oct 2026

Logarithmic Pruning is All You Need

arXiv 2006.12156 ↗First posted 22 Jun 2020cs.LG stat.MLcited by 71

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Abstract (arXiv)

The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large network. An even stronger conjecture has been proven recently: Every sufficiently overparameterized network contains a subnetwork that, at random initialization, but without training, achieves comparable accuracy to the trained large network. This latter result, however, relies on a number of strong assumptions and guarantees a polynomial factor on the size of the large network compared to the target function. In this work, we remove the most limiting assumptions of this previous work while providing significantly tighter bounds:the overparameterized network only needs a logarithmic factor (in all variables but depth) number of neurons per weight of the target subnetwork.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 17; 2023: 13; 2024: 11; 2025: 6; 2026: 7.

71 papers.

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  1. 2023DepGraph: Towards Any Structural Pruningcited by 202
  2. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 200
  3. 2023Structured Pruning for Deep Convolutional Neural Networks: A surveycited by 122
  4. 2022Monarch: Expressive Structured Matrices for Efficient and Accurate Trainingcited by 100
  5. 2021Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Modelscited by 75
  6. 2021A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustnesscited by 62
  7. 2021Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Networkcited by 55
  8. 2021Recent Advances on Neural Network Pruning at Initializationcited by 41
  9. 2022The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networkscited by 41
  10. 2022Rare Gems: Finding Lottery Tickets at Initializationcited by 34
  11. 2021On the Existence of Universal Lottery Ticketscited by 30
  12. 2022Why Random Pruning Is All We Need to Start Sparsecited by 26
38 more
  1. 2024A Survey of Lottery Ticket Hypothesiscited by 23
  2. 2022Most Activation Functions Can Win the Lottery Without Excessive Depthcited by 21
  3. 2021Pruning Randomly Initialized Neural Networks with Iterative Randomizationcited by 20
  4. 2022A General Framework For Proving The Equivariant Strong Lottery Ticket Hypothesiscited by 19
  5. 2022Convolutional and Residual Networks Provably Contain Lottery Ticketscited by 16
  6. 2024On the Surprising Effectiveness of Attention Transfer for Vision Transformerscited by 16
  7. 2021A Probabilistic Approach to Neural Network Pruningcited by 15
  8. 2024Masks, Signs, And Learning Rate Rewindingcited by 15
  9. 2021Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networkscited by 14
  10. 2021Plant 'n' Seek: Can You Find the Winning Ticket?cited by 13
  11. 2022Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Surveycited by 12
  12. 2024Mask in the Mirror: Implicit Sparsificationcited by 12
  13. 2021Lottery Jackpots Exist in Pre-trained Modelscited by 10
  14. 2021SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruningcited by 9
  15. 2023Sparse Binary Transformers for Multivariate Time Series Modelingcited by 8
  16. 2024Optimal Recurrent Network Topologies for Dynamical Systems Reconstructioncited by 8
  17. 2022The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Networkcited by 7
  18. 2024Survival of the Fittest Representation: A Case Study with Modular Additioncited by 7
  19. 2022OptG: Optimizing Gradient-driven Criteria in Network Sparsitycited by 5
  20. 2022A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximationcited by 5
  21. 2023Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computationcited by 5
  22. 2021How much pre-training is enough to discover a good subnetwork?cited by 4
  23. 2021Finding Everything within Random Binary Networkscited by 4
  24. 2021Lottery Tickets with Nonzero Biasescited by 4
  25. 2022Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networkscited by 4
  26. 2022LOFT: Finding Lottery Tickets through Filter-wise Trainingcited by 3
  27. 2022Strong Lottery Ticket Hypothesis with varepsilon--perturbationcited by 3
  28. 2023Randomly Initialized Subnetworks with Iterative Weight Recyclingcited by 3
  29. 2023Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Networkcited by 3
  30. 2023Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Ticketscited by 3
  31. 2024On the Sparsity of the Strong Lottery Ticket Hypothesiscited by 3
  32. 2025PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Modelscited by 3
  33. 2021Efficient Inference via Universal LSH Kernelcited by 2
  34. 2021Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directionscited by 2
  35. 2022Extracting Effective Subnetworks with Gumbel-Softmaxcited by 2
  36. 2023Pruning at Initialization -- A Sketching Perspectivecited by 2
  37. 2023Quantifying lottery tickets under label noise: accuracy, calibration, and complexitycited by 2
  38. 2023Multicoated and Folded Graph Neural Networks with Strong Lottery Ticketscited by 2

50 most cited of 71 All 71 (JSON)