Bayesian Lottery Ticket Hypothesis

• URL: http://arxiv.org/abs/2602.18825v1
• Date: Sat, 21 Feb 2026 12:52:20 GMT
• Title: Bayesian Lottery Ticket Hypothesis
• Authors: Nicholas Kuhn, Arvid Weyrauch, Lars Heyen, Achim Streit, Markus Götz, Charlotte Debus,
• Abstract summary: Lottery Ticket Hypothesis (LTH) posits the existence of sparseworks that can train to the same or even surpassing accuracy as the original dense network.<n>We investigate the defining characteristics of Bayesian lottery tickets, and extend our study towards a method connecting BNNs with deterministic Lottery Tickets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• S$^2$NN: Sub-bit Spiking Neural Networks [53.08060832135342]
  Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence.<n>Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks.<n>We propose Sub-bit Spiking Neural Networks (S$2$NNs) that represent weights with less than one bit.
  arXiv  Detail & Related papers  (2025-09-29T04:17:44Z)
• Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference [1.2722697496405464]
  We propose a new node-sparse BNN model which has good theoretical properties and is computationally feasible. We prove that the posterior concentration rate to the true model is near minimax optimal and adaptive to the smoothness of the true model. In addition, we develop a novel MCMC algorithm which makes the Bayesian inference of the node-sparse BNN model feasible in practice.
  arXiv  Detail & Related papers  (2023-05-24T06:16:11Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Dual Lottery Ticket Hypothesis [71.95937879869334]
  Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its capacity. In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark. We propose a simple sparse network training strategy, Random Sparse Network Transformation (RST), to substantiate our DLTH.
  arXiv  Detail & Related papers  (2022-03-08T18:06:26Z)
• Model Architecture Adaption for Bayesian Neural Networks [9.978961706999833]
  We show a novel network architecture search (NAS) that optimize BNNs for both accuracy and uncertainty. In our experiments, the searched models show comparable uncertainty ability and accuracy compared to the state-of-the-art (deep ensemble)
  arXiv  Detail & Related papers  (2022-02-09T10:58:50Z)
• Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks [79.74580058178594]
  We analyze the performance of training a pruned neural network by analyzing the geometric structure of the objective function. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned.
  arXiv  Detail & Related papers  (2021-10-12T01:11:07Z)
• How much pre-training is enough to discover a good subnetwork? [10.699603774240853]
  We mathematically analyze the amount of dense network pre-training needed for a pruned network to perform well. We find a simple theoretical bound in the number of gradient descent pre-training iterations on a two-layer, fully-connected network. Experiments with larger datasets require more pre-training forworks obtained via pruning to perform well.
  arXiv  Detail & Related papers  (2021-07-31T15:08:36Z)
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration [74.5509794733707]
  We present a novel guided learning paradigm from real-valued to distill binary networks on the final prediction distribution. Our proposed method can boost the simple contrastive learning baseline by an absolute gain of 5.515% on BNNs. Our method achieves substantial improvement over the simple contrastive learning baseline, and is even comparable to many mainstream supervised BNN methods.
  arXiv  Detail & Related papers  (2021-02-17T18:59:28Z)
• Good Students Play Big Lottery Better [84.6111281091602]
  Lottery ticket hypothesis suggests that a dense neural network contains a sparse sub-network that can match the test accuracy of the original dense net. Recent studies demonstrate that a sparse sub-network can still be obtained by using a rewinding technique. This paper proposes a new, simpler and yet powerful technique for re-training the sub-network, called "Knowledge Distillation ticket" (KD ticket)
  arXiv  Detail & Related papers  (2021-01-08T23:33:53Z)
• Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification [10.727102755903616]
  We aim for efficient deep BNNs amenable to complex computer vision architectures. We achieve this by leveraging variational autoencoders (VAEs) to learn the interaction and the latent distribution of the parameters at each network layer. Our approach, Latent-Posterior BNN (LP-BNN), is compatible with the recent BatchEnsemble method, leading to highly efficient (in terms of computation and memory during both training and testing) ensembles.
  arXiv  Detail & Related papers  (2020-12-04T19:50:09Z)
• The Lottery Ticket Hypothesis for Pre-trained BERT Networks [137.99328302234338]
  In natural language processing (NLP), enormous pre-trained models like BERT have become the standard starting point for training. In parallel, work on the lottery ticket hypothesis has shown that models for NLP and computer vision contain smaller matchingworks capable of training in isolation to full accuracy. We combine these observations to assess whether such trainable, transferrableworks exist in pre-trained BERT models.
  arXiv  Detail & Related papers  (2020-07-23T19:35:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.