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Orthogonally to Bayesian neural nets\nthat indirectly infer prediction uncertainty through weight uncertainties, we\npropose explicit modeling of the same using the theory of subjective logic. By\nplacing a Dirichlet distribution on the class probabilities, we treat\npredictions of a neural net as subjective opinions and learn the function that\ncollects the evidence leading to these opinions by a deterministic neural net\nfrom data. The resultant predictor for a multi-class classification problem is\nanother Dirichlet distribution whose parameters are set by the continuous\noutput of a neural net. We provide a preliminary analysis on how the\npeculiarities of our new loss function drive improved uncertainty estimation.\nWe observe that our method achieves unprecedented success on detection of\nout-of-distribution queries and endurance against adversarial perturbations.","url_abs":"http://arxiv.org/abs/1806.01768v3","url_pdf":"http://arxiv.org/pdf/1806.01768v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/Cogito2012/DEAR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/atilberk/evidential-deep-learning-to-quantify-classification-uncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/dougbrion/pytorch-classification-uncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/jun-cen/psl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/ms96590/-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/ms96590/pytorch-classification-uncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/renato145/ClassificationUncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/teddykoker/evidential-learning-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/ENSTA-U2IS-AI/torch-uncertainty","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"evidential-deep-learning-to-quantify","repo_url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.01768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.01768"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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