Student's t-distribution outperforms Gaussian in Bayesian Neural Networks

Researchers have explored the impact of different likelihood distributions on the performance of Bayesian Neural Networks (BNNs). While Gaussian distributions are commonly used for modeling uncertainty in BNNs due to computational ease, this study investigates whether alternative distributions can yield better results. The findings indicate that using a Student's t-distribution for the likelihood function consistently improves predictive performance over a Gaussian assumption, regardless of the data or network architecture. Furthermore, the Student's t-distribution can also potentially reduce training times while remaining simple to implement. AI

IMPACT This research could lead to more accurate and efficient Bayesian Neural Networks by suggesting a superior distribution for modeling uncertainty.

RANK_REASON Academic paper detailing a novel method for improving model performance. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Bayesian Neural Networks
• evidence lower bound
• Gaussian function
• Student's t-distribution
• Variational Inference

• paper
• model release

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