# Bayesian Lottery Ticket Hypothesis

arXiv: https://arxiv.org/abs/2602.18825 · HTML: https://mlsanity.com/paper/2602.18825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 48 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Variational Dropout and the Local Reparameterization Trick](https://mlsanity.com/paper/1506.02557/) (2015)
- [Picking Winning Tickets Before Training by Preserving Gradient Flow](https://mlsanity.com/paper/2002.07376/) (2020)
- [What Are Bayesian Neural Network Posteriors Really Like?](https://mlsanity.com/paper/2104.14421/) (2021)
- [Comparing Rewinding and Fine-tuning in Neural Network Pruning](https://mlsanity.com/paper/2003.02389/) (2020)
- [A Unified Lottery Ticket Hypothesis for Graph Neural Networks](https://mlsanity.com/paper/2102.06790/) (2021)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Fast Sparse ConvNets](https://mlsanity.com/paper/1911.09723/) (2019)
- [EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets](https://mlsanity.com/paper/2101.00063/) (2020)
- [Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP](https://mlsanity.com/paper/1906.02768/) (2019)
- [Stochastic gradient Markov chain Monte Carlo](https://mlsanity.com/paper/1907.06986/) (2019)
- [Model Selection in Bayesian Neural Networks via Horseshoe Priors](https://mlsanity.com/paper/1705.10388/) (2017)
- [Dynamic Sparse Training with Structured Sparsity](https://mlsanity.com/paper/2305.02299/) (2023)
- [Convergence Rates of Variational Inference in Sparse Deep Learning](https://mlsanity.com/paper/1908.04847/) (2019)
- [A Survey of Lottery Ticket Hypothesis](https://mlsanity.com/paper/2403.04861/) (2024)
- [ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks](https://mlsanity.com/paper/2401.10341/) (2024)
- [Training Bayesian Neural Networks with Sparse Subspace Variational Inference](https://mlsanity.com/paper/2402.11025/) (2024)
- [Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning](https://mlsanity.com/paper/2004.09817/) (2020)
- [Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks](https://mlsanity.com/paper/2308.09104/) (2023)
- [Learning to Explore for Stochastic Gradient MCMC](https://mlsanity.com/paper/2408.09140/) (2024)
- [Magnitude and Uncertainty Pruning Criterion for Neural Networks](https://mlsanity.com/paper/1912.04845/) (2019)
- [Functional Stochastic Gradient MCMC for Bayesian Neural Networks](https://mlsanity.com/paper/2409.16632/) (2024)

## Built on by (most cited first, 2 of 2)

- [Neural Feature Governance: Extending Atom Prevalence](https://mlsanity.com/paper/2607.21671/) (2026)
- [Calibration-Preserving Pruning: Compression as a Reliability Contract](https://mlsanity.com/paper/2608.23744/) (2026)

## Abstract (arXiv)

Bayesian neural networks (BNNs) are a useful tool for uncertainty quantification, but require substantially more computational resources than conventional neural networks. For non-Bayesian networks, the Lottery Ticket Hypothesis (LTH) posits the existence of sparse subnetworks that can train to the same or even surpassing accuracy as the original dense network. Such sparse networks can lower the demand for computational resources at inference, and during training. The existence of the LTH and corresponding sparse subnetworks in BNNs could motivate the development of sparse training algorithms and provide valuable insights into the underlying training process. Towards this end, we translate the LTH experiments to a Bayesian setting using common computer vision models. We investigate the defining characteristics of Bayesian lottery tickets, and extend our study towards a transplantation method connecting BNNs with deterministic Lottery Tickets. We generally find that the LTH holds in BNNs, and winning tickets of matching and surpassing accuracy are present independent of model size, with degradation at very high sparsities. However, the pruning strategy should rely primarily on magnitude, secondly on standard deviation. Furthermore, our results demonstrate that models rely on mask structure and weight initialization to varying degrees.