Efficient Bayesian Updates for Deep Learning via Laplace Approximations

• URL: http://arxiv.org/abs/2210.06112v2
• Date: Fri, 12 Jul 2024 15:23:28 GMT
• Title: Efficient Bayesian Updates for Deep Learning via Laplace Approximations
• Authors: Denis Huseljic, Marek Herde, Lukas Rauch, Paul Hahn, Zhixin Huang, Daniel Kottke, Stephan Vogt, Bernhard Sick,
• Abstract summary: We propose a novel Bayesian update method for deep neural networks. We leverage second-order optimization techniques on the Gaussian posterior distribution of a Laplace approximation. A large-scale evaluation study confirms that our updates are a fast and competitive alternative to costly retraining.
• Score: 1.5996841879821277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since training deep neural networks takes significant computational resources, extending the training dataset with new data is difficult, as it typically requires complete retraining. Moreover, specific applications do not allow costly retraining due to time or computational constraints. We address this issue by proposing a novel Bayesian update method for deep neural networks by using a last-layer Laplace approximation. Concretely, we leverage second-order optimization techniques on the Gaussian posterior distribution of a Laplace approximation, computing the inverse Hessian matrix in closed form. This way, our method allows for fast and effective updates upon the arrival of new data in a stationary setting. A large-scale evaluation study across different data modalities confirms that our updates are a fast and competitive alternative to costly retraining. Furthermore, we demonstrate its applicability in a deep active learning scenario by using our update to improve existing selection strategies.

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