Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks

• URL: http://arxiv.org/abs/2601.21983v1
• Date: Thu, 29 Jan 2026 16:59:31 GMT
• Title: Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
• Authors: Andrew Millard, Joshua Murphy, Peter Green, Simon Maskell,
• Abstract summary: Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN)<n>One such approximation - variational inference - is computationally efficient when using mini-batch gradient descent.<n>We find that we can achieve up to $6times$ faster training with minimal loss in accuracy on benchmark image classification problems using NNs.
• Score: 1.8129328638036128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN). Exact posteriors are usually intractable, in which case approximations can be employed. One such approximation - variational inference - is computationally efficient when using mini-batch stochastic gradient descent as subsets of the data are used for likelihood and gradient evaluations, though the approach relies on the selection of a variational distribution which sufficiently matches the form of the posterior. Particle-based methods such as Markov chain Monte Carlo and Sequential Monte Carlo (SMC) do not assume a parametric family for the posterior by typically require higher computational cost. These sampling methods typically use the full-batch of data for likelihood and gradient evaluations, which contributes to this computational expense. We explore several methods of gradually introducing more mini-batches of data (data annealing) into likelihood and gradient evaluations of an SMC sampler. We find that we can achieve up to $6\times$ faster training with minimal loss in accuracy on benchmark image classification problems using NNs.

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