Python librarY for UNcertainty analysis in liGhtwEight desiGn with IntervalS and fuzzy numberS
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Updated
Apr 11, 2023 - Python
Python librarY for UNcertainty analysis in liGhtwEight desiGn with IntervalS and fuzzy numberS
UQ Group (Director: Hadi Meidani)
❓Meter Accuracy - Portable and open repository of various DMMs accuracy expressed in uncertainty based on range, reading and absolute offset.
Simple dead reckoning example in one dimension
[NeurIPSW 2022] On the Ramifications of Human Label Uncertainty
Research project: The Impact of Uncertainty on Monetary Transmission - Evidence from the US
A library for the representation and propagation of uncertainty
Truth Discovery Promotes Uncertainty Calibration of DNNs (UAI 2021)
A hybrid model for activity recognition and uncertainty handling
3-dimensional version of the Kiviat or Spyder plot
Bot playing to microRTS (github.com/santiontanon/microrts) and exploiting GHOST (github.com/richoux/GHOST) to solver optimization problems under uncertainty
Contrastive Normalizing Flow: Robust Uncertainty Estimation Under Distributional Shifts
Python codes to implement the PFGE algorithm
Bootplot is a package for black-box uncertainty visualization.
PointNetVLAD-FiLM: Implicit ensemble implementation using FiLM-Ensemble
This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic.
We provide two notebooks that enable users to explore and experiment with some BDL techniques as Ensembles, MC Dropout and Laplace Approximation. In this way, they allow you to intuitively visualize the main differences among them in a Simulated Dataset and Boston Dataset.
Active Learning is a subset of ML wherein a learner aims to minimize the sample complexity with the means of interactively querying the user/oracle for the ground label. We implement and benchmark 2 active learning strategies and 1 application strategy from Active Learning literature.
An algorithm for bayesian regression with uncertainties in all dimensions.
Scripts to reproduce results within the following manuscript: Perez, I., Skalski, P., Barns-Graham, A., Wong, J. and Sutton, D. (2022) Attribution of Predictive Uncertainties in Classification Models, 38th Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022.
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