Evidential Turing Processes
Melih Kandemir, Abdullah Akg\"ul, Manuel Haussmann, Gozde Unal

TL;DR
This paper introduces a novel probabilistic classifier that combines Evidential Deep Learning, Neural Processes, and Neural Turing Machines to achieve reliable uncertainty estimation, calibration, and out-of-domain detection in classification tasks.
Contribution
It presents an original unified model capable of total uncertainty quantification, excelling in calibration, out-of-domain detection, and predictive reliability with a single predictor.
Findings
Outperforms existing methods in calibration and out-of-domain detection
Achieves reliable predictive uncertainties across five classification tasks
Provides an efficient and implementation-friendly approach for safety-critical applications
Abstract
A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them. We introduce an original combination of Evidential Deep Learning, Neural Processes, and Neural Turing Machines capable of providing all three essential properties mentioned above for total uncertainty quantification. We observe our method on five classification tasks to be the only one that can excel all three aspects of total calibration with a single standalone predictor. Our unified solution delivers an implementation-friendly and compute efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI) · Machine Learning and Algorithms
