Bayesian Lottery Ticket Hypothesis
arXiv:2602.18825v1 Announce Type: new Abstract: 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 Tic
arXiv:2602.18825v1 Announce Type: new Abstract: 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.
Executive Summary
This article explores the Bayesian Lottery Ticket Hypothesis, which posits the existence of sparse subnetworks in Bayesian neural networks that can achieve comparable or superior accuracy to dense networks. The authors translate the Lottery Ticket Hypothesis to a Bayesian setting, investigating the characteristics of Bayesian lottery tickets and developing a transplantation method connecting Bayesian neural networks with deterministic Lottery Tickets. The results show that the Lottery Ticket Hypothesis holds in Bayesian neural networks, with winning tickets present across various model sizes, although the pruning strategy and mask structure play significant roles.
Key Points
- ▸ The Bayesian Lottery Ticket Hypothesis is applicable to Bayesian neural networks
- ▸ Winning tickets are present across various model sizes, with some degradation at high sparsities
- ▸ Pruning strategy and mask structure significantly impact the performance of sparse Bayesian neural networks
Merits
Methodological Contribution
The article contributes to the development of sparse training algorithms for Bayesian neural networks, which can reduce computational resource demands.
Demerits
Limited Exploration of Hyperparameters
The study could benefit from a more extensive exploration of hyperparameters and their impact on the performance of sparse Bayesian neural networks.
Expert Commentary
The article provides a valuable contribution to the understanding of the Bayesian Lottery Ticket Hypothesis and its implications for Bayesian neural networks. The results demonstrate the potential for sparse Bayesian neural networks to achieve comparable or superior accuracy to dense networks, while reducing computational resource demands. However, further research is needed to fully explore the potential of this hypothesis and its applications in various domains. The study's findings have significant implications for the development of more efficient and accurate Bayesian neural networks, which can facilitate advancements in fields such as computer vision and natural language processing.
Recommendations
- ✓ Future studies should investigate the applicability of the Bayesian Lottery Ticket Hypothesis to various domains and tasks, including natural language processing and reinforcement learning.
- ✓ Researchers should explore the development of more efficient pruning strategies and transplantation methods to further improve the performance of sparse Bayesian neural networks.
