mlsanityData to 7 Oct 2026

On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks

arXiv 2102.11062 ↗First posted 22 Feb 2021cs.LG stat.MLcited by 8

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Abstract (arXiv)

Bayesian neural networks (BNNs) are making significant progress in many research areas where decision-making needs to be accompanied by uncertainty estimation. Being able to quantify uncertainty while making decisions is essential for understanding when the model is over-/under-confident, and hence BNNs are attracting interest in safety-critical applications, such as autonomous driving, healthcare, and robotics. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their increased memory and compute costs. In this work, we investigate quantisation of BNNs by compressing 32-bit floating-point weights and activations to their integer counterparts, that has already been successful in reducing the compute demand in standard pointwise neural networks. We study three types of quantised BNNs, we evaluate them under a wide range of different settings, and we empirically demonstrate that a uniform quantisation scheme applied to BNNs does not substantially decrease their quality of uncertainty estimation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 22 Feb 2021.

Builds on

16 of 30 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2017On Calibration of Modern Neural Networkscited by 4,860
  4. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  5. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  6. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  7. 2015Weight Uncertainty in Neural Networkscited by 554
  8. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 507
  9. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 341
  10. 2013Black Box Variational Inferencecited by 317
  11. 2019Low-bit Quantization of Neural Networks for Efficient Inferencecited by 185
  12. 2020Bayesian Bits: Unifying Quantization and Pruningcited by 93
4 more
  1. 2018VIBNN: Hardware Acceleration of Bayesian Neural Networkscited by 19
  2. 2017A Survey of FPGA-Based Neural Network Acceleratorcited by 17
  3. 2017Variational Gaussian Dropout is not Bayesiancited by 13
  4. 2019Sampling-Free Learning of Bayesian Quantized Neural Networkscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2021Bayesian Learning via Neural Schr"odinger-F"ollmer Flowscited by 56
  2. 2022Low-Precision Stochastic Gradient Langevin Dynamicscited by 12
  3. 2025Stochastic Weight Sharing for Bayesian Neural Networkscited by 2
  4. 2024Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systemscited by 1
  5. 2024Uncertainty Quantification in Detecting Choroidal Metastases on MRI via Evolutionary Strategiescited by 0
  6. 2024Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGAcited by 0
  7. 2025Uncertainty-Preserving QBNNs: Multi-Level Quantization of SVI-Based Bayesian Neural Networks for Image Classificationcited by 0
  8. 2026Constrained Co-Design for Photonic Bayesian Neural Networkscited by 0