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Related Experiment Video

Updated: Jul 25, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

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On Sequential Bayesian Inference for Continual Learning.

Samuel Kessler¹, Adam Cobb², Tim G J Rudner³

¹Department of Engineering Science, University of Oxford, Oxford OX2 6ED, UK.

Entropy (Basel, Switzerland)

|June 28, 2023

Summary

This summary is machine-generated.

Sequential Bayesian inference struggles with catastrophic forgetting in Bayesian neural networks. A new Prototypical Bayesian Continual Learning baseline shows competitive performance on computer vision benchmarks.

Keywords:

Bayesian deep learning Bayesian neural networks continual learning lifelong learning sequential Bayesian inference

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Continual learning aims to train models on sequential tasks without forgetting past knowledge.
Sequential Bayesian inference offers a theoretical framework for continual learning by using previous posteriors as informative priors.
Bayesian neural networks are explored for their potential in continual learning due to their probabilistic nature.

Purpose of the Study:

To assess the effectiveness of sequential Bayesian inference in preventing catastrophic forgetting in Bayesian neural networks.
To investigate the challenges of applying sequential Bayesian inference with Hamiltonian Monte Carlo in neural networks.
To propose and evaluate a novel Bayesian continual learning method.

Main Methods:

Sequential Bayesian inference was performed using Hamiltonian Monte Carlo.
Posterior distributions were approximated using density estimators on Hamiltonian Monte Carlo samples.
Analytical examples were used to study model misspecification and data imbalance effects.
A new method, Prototypical Bayesian Continual Learning, was proposed and evaluated.

Main Results:

Directly applying sequential Bayesian inference with Hamiltonian Monte Carlo failed to prevent catastrophic forgetting in Bayesian neural networks.
Model misspecification and data imbalances were identified as significant challenges in continual learning.
The proposed Prototypical Bayesian Continual Learning baseline achieved competitive results on class-incremental computer vision benchmarks.

Conclusions:

Sequential Bayesian inference over neural network weights is insufficient for robust continual learning.
Probabilistic models of the continual learning generative process are needed.
Prototypical Bayesian Continual Learning presents a promising and effective approach for class-incremental continual learning.