Evidential Turing Processes

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 deep neural nets.

accepted at ICLR2022; camera ready version

References in corpus (8)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• On Calibration of Modern Neural Networks
• Evidential Deep Learning to Quantify Classification Uncertainty
• Accurate Uncertainties for Deep Learning Using Calibrated Regression
• Variational Dropout Sparsifies Deep Neural Networks
• Sequential Neural Processes
• The Functional Neural Process