Uncertainty in Neural Relational Inference Trajectory Reconstruction

• URL: http://arxiv.org/abs/2006.13666v2
• Date: Thu, 25 Jun 2020 10:02:34 GMT
• Title: Uncertainty in Neural Relational Inference Trajectory Reconstruction
• Authors: Vasileios Karavias, Ben Day, Pietro Li\`o
• Abstract summary: We extend the Neural Inference model to output both a mean standard deviation for each component of a space vector and an appropriate loss function. We show that the physical meaning of the variables is important when considering the uncertainty and demonstrate the existence of pathological local minima.
• Score: 3.4806267677524896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural networks used for multi-interaction trajectory reconstruction lack the ability to estimate the uncertainty in their outputs, which would be useful to better analyse and understand the systems they model. In this paper we extend the Factorised Neural Relational Inference model to output both a mean and a standard deviation for each component of the phase space vector, which together with an appropriate loss function, can account for uncertainty. A variety of loss functions are investigated including ideas from convexification and a Bayesian treatment of the problem. We show that the physical meaning of the variables is important when considering the uncertainty and demonstrate the existence of pathological local minima that are difficult to avoid during training.

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