Hi everyone, we have guest speakers to present their works this Thursday. You are all cordially invited to the AMLab Seminar on December 10th at 4:00 p.m. CET on Zoom, where Javier Antorán and James Allingham will give a talk titled ” Depth Uncertainty in Neural Networks “.
Title : Depth Uncertainty in Neural Networks
Abstract : Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines.
Paper Link: https://arxiv.org/pdf/2006.08437.pdf
To gain more deep insights into Uncertainty in Deep Neural Networks, feel free to join and discuss it! See you there!