Media Summary: It is a part of IAH's (International Association of Hydrogeologists) webinar series which held on 24 February 2021. The original ... A short video on what the above paper discusses: - Neural networks predictions are unreliable when the input sample is out of the training data distribution or corrupted by noise.
A Novel Uncertainty Estimation Framework - Detailed Analysis & Overview
It is a part of IAH's (International Association of Hydrogeologists) webinar series which held on 24 February 2021. The original ... A short video on what the above paper discusses: - Neural networks predictions are unreliable when the input sample is out of the training data distribution or corrupted by noise. In this work, we introduce a new technique that combines two popular methods to Machine learning models make predictions, but real-world systems also need to know how unsure the model is. That is the ... Please see for more information on the topics covered in this video.
Authors: Yukun Ding, Jinglan Liu, Jinjun Xiong, Yiyu Shi Description: Accurately estimating Video presentation for the ICML 2024 accepted paper. During our session on the 12.12.2022 Nikita Durasov presented his CVPR 2021 work. In this work the authors proposed How do we quantify how certain our model is? Discusses Epistemic vs Aleatoric Authors: Ang Nan Gu, Michael Tsang, Hooman Vaseli, Purang Abolmaesumi, Teresa Tsang Paper Link: ...
![[CVPR] Masksembles for Uncertainty Estimation](https://i.ytimg.com/vi/YWKVdn3kLp0/mqdefault.jpg)
