# Rare Gems: Finding Lottery Tickets at Initialization

arXiv: https://arxiv.org/abs/2202.12002 · HTML: https://mlsanity.com/paper/2202.12002/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 52 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Linear Mode Connectivity and the Lottery Ticket Hypothesis](https://mlsanity.com/paper/1912.05671/) (2019)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Pruning neural networks without any data by iteratively conserving synaptic flow](https://mlsanity.com/paper/2006.05467/) (2020)
- [The State of Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1902.09574/) (2019)
- [Picking Winning Tickets Before Training by Preserving Gradient Flow](https://mlsanity.com/paper/2002.07376/) (2020)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://mlsanity.com/paper/2005.07683/) (2020)
- [The Lottery Ticket Hypothesis for Pre-trained BERT Networks](https://mlsanity.com/paper/2007.12223/) (2020)
- [Comparing Rewinding and Fine-tuning in Neural Network Pruning](https://mlsanity.com/paper/2003.02389/) (2020)
- [What's Hidden in a Randomly Weighted Neural Network?](https://mlsanity.com/paper/1911.13299/) (2019)
- [Proving the Lottery Ticket Hypothesis: Pruning is All You Need](https://mlsanity.com/paper/2002.00585/) (2020)
- [Pruning Neural Networks at Initialization: Why are We Missing the Mark?](https://mlsanity.com/paper/2009.08576/) (2020)
- [A Unified Lottery Ticket Hypothesis for Graph Neural Networks](https://mlsanity.com/paper/2102.06790/) (2021)
- [Winning the Lottery with Continuous Sparsification](https://mlsanity.com/paper/1912.04427/) (2019)
- [Optimal Lottery Tickets via SubsetSum: Logarithmic Over-Parameterization is Sufficient](https://mlsanity.com/paper/2006.07990/) (2020)
- [Logarithmic Pruning is All You Need](https://mlsanity.com/paper/2006.12156/) (2020)
- [Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot](https://mlsanity.com/paper/2009.11094/) (2020)
- [LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets](https://mlsanity.com/paper/2008.03371/) (2020)

## Built on by (most cited first, 30 of 34)

- [Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models](https://mlsanity.com/paper/2406.02924/) (2024)
- [Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?](https://mlsanity.com/paper/2210.03044/) (2022)
- [Why Random Pruning Is All We Need to Start Sparse](https://mlsanity.com/paper/2210.02412/) (2022)
- [Cuttlefish: Low-Rank Model Training without All the Tuning](https://mlsanity.com/paper/2305.02538/) (2023)
- [One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2305.17682/) (2023)
- [Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training](https://mlsanity.com/paper/2306.12230/) (2023)
- [Mask in the Mirror: Implicit Sparsification](https://mlsanity.com/paper/2408.09966/) (2024)
- [FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training](https://mlsanity.com/paper/2411.07837/) (2024)
- [On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks](https://mlsanity.com/paper/2203.14328/) (2022)
- [How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective](https://mlsanity.com/paper/2306.05857/) (2023)
- [Maestro: Uncovering Low-Rank Structures via Trainable Decomposition](https://mlsanity.com/paper/2308.14929/) (2023)
- [No Free Prune: Information-Theoretic Barriers to Pruning at Initialization](https://mlsanity.com/paper/2402.01089/) (2024)
- [Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction](https://mlsanity.com/paper/2406.04934/) (2024)
- [Can We Find Strong Lottery Tickets in Generative Models?](https://mlsanity.com/paper/2212.08311/) (2022)
- [LOFT: Finding Lottery Tickets through Filter-wise Training](https://mlsanity.com/paper/2210.16169/) (2022)
- [Pruning Before Training May Improve Generalization, Provably](https://mlsanity.com/paper/2301.00335/) (2023)
- [DIET: Customized Slimming for Incompatible Networks in Sequential Recommendation](https://mlsanity.com/paper/2406.08804/) (2024)
- [Forward Once for All: Structural Parameterized Adaptation for Efficient Cloud-coordinated On-device Recommendation](https://mlsanity.com/paper/2501.02837/) (2025)
- [Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations](https://mlsanity.com/paper/2305.16886/) (2023)
- [Pruning at Initialization -- A Sketching Perspective](https://mlsanity.com/paper/2305.17559/) (2023)
- [Multicoated and Folded Graph Neural Networks with Strong Lottery Tickets](https://mlsanity.com/paper/2312.03236/) (2023)
- [Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation](https://mlsanity.com/paper/2405.11614/) (2024)
- [Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at Initialization](https://mlsanity.com/paper/2406.05288/) (2024)
- [Nerva: a Truly Sparse Implementation of Neural Networks](https://mlsanity.com/paper/2407.17437/) (2024)
- [D2Prune: Sparsifying Large Language Models via Dual Taylor Expansion and Attention Distribution Awareness](https://mlsanity.com/paper/2601.09176/) (2026)
- [Pruning neural network models for gene regulatory dynamics using data and domain knowledge](https://mlsanity.com/paper/2403.04805/) (2024)
- [What Scalable Second-Order Information Knows for Pruning at Initialization](https://mlsanity.com/paper/2502.11450/) (2025)
- [Find A Winning Sign: Sign Is All We Need to Win the Lottery](https://mlsanity.com/paper/2504.05357/) (2025)
- [Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data](https://mlsanity.com/paper/2601.22141/) (2026)
- [Resource-constrained knowledge diffusion processes inspired by human peer learning](https://mlsanity.com/paper/2312.00660/) (2023)

## Abstract (arXiv)

Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frankle & Carbin conjecture that we can avoid this by training "lottery tickets", i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work by Frankle et al. and Su et al. presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open problem. In this work, we resolve this open problem by proposing Gem-Miner which finds lottery tickets at initialization that beat current baselines. Gem-Miner finds lottery tickets trainable to accuracy competitive or better than Iterative Magnitude Pruning (IMP), and does so up to 19× faster.