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However, DNNs can make unexpected, incorrect, yet overconfident predictions, leading to serious consequences in high-stakes applications such as autonomous driving, medical diagnosis, and disaster response. Uncertainty quantification (UQ) estimates the confidence of DNN predictions in addition to their accuracy. In recent years, many UQ methods have been developed for DNNs. It is valuable to systematically categorize these methods and compare their strengths and limitations. Existing surveys mostly categorize UQ methodologies by neural network architecture or Bayesian formulation, while overlooking the uncertainty sources each method addresses, making it difficult to select an appropriate approach in practice. To fill this gap, this article presents a taxonomy of UQ methods for DNNs based on uncertainty sources (e.g., data versus model uncertainty). We summarize the advantages and disadvantages of each category, and illustrate how UQ can be applied to machine learning problems (e.g., active learning, out-of-distribution robustness, and deep reinforcement learning). We also identify future research directions, including UQ for large language models (LLMs), AI-driven scientific simulations, and DNNs with structured outputs.<\/jats:p>","DOI":"10.1145\/3786319","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T09:35:02Z","timestamp":1766396102000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["A Survey on Uncertainty Quantification Methods for Deep Learning"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8115-1115","authenticated-orcid":false,"given":"Wenchong","family":"He","sequence":"first","affiliation":[{"name":"Department of Computer & Information Science & Engineering, University of Florida","place":["Gainesville, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-6976","authenticated-orcid":false,"given":"Zhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Science & Engineering, University of Florida","place":["Gainesville, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1466-9421","authenticated-orcid":false,"given":"Tingsong","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Science & Engineering, University of Florida","place":["Gainesville, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4419-3155","authenticated-orcid":false,"given":"Zelin","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Science & Engineering, University of Florida","place":["Gainesville, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8979-477X","authenticated-orcid":false,"given":"Yukun","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tufts University","place":["Medford, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"e_1_3_2_3_2","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya Florencia Leoni Aleman Diogo Almeida Janko Altenschmidt Sam Altman Shyamal Anadkat et\u00a0al. 2023. 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