Results 301 to 310 of about 3,113,242 (314)

FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information

arXiv.org, 2023
This study addresses the challenging problem of active view selection and uncertainty quantification within the domain of Radiance Fields. Neural Radiance Fields (NeRF) have greatly advanced image rendering and reconstruction, but the cost of acquiring ...
Wen Jiang, Boshu Lei, Kostas Daniilidis
semanticscholar   +1 more source

Uncertainty Quantification in Deep Learning

Knowledge Discovery and Data Mining, 2023
Deep neural networks (DNNs) have achieved enormous success in a wide range of domains, such as computer vision, natural language processing and scientific areas.
Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B. Prakash, Chao Zhang   +4 more
semanticscholar   +1 more source

Benchmarking LLMs via Uncertainty Quantification

Neural Information Processing Systems
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F. Wong, Emine Yilmaz, Shuming Shi, Zhaopeng Tu   +7 more
semanticscholar   +1 more source

A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Structural And Multidisciplinary Optimization, 2022
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively
Adam Thelen, Xiaoge Zhang, Olga Fink, Yan Lu, Sayan Ghosh, B. Youn, Michael D. Todd, S. Mahadevan, Chao Hu, Zhen Hu   +9 more
semanticscholar   +1 more source

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Annual Meeting of the Association for Computational Linguistics
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output. Such hallucinations can be dangerous, as occasional factual inaccuracies in the generated text might be obscured by the rest of the output ...
Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, Sergey Petrakov, Haonan Li, Hamdy Mubarak, Evgenii Tsymbalov, Gleb Kuzmin, Alexander Panchenko, Timothy Baldwin, Preslav Nakov, Maxim Panov   +11 more
semanticscholar   +1 more source

A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration

IEEE Transactions on Industrial Informatics, 2022
In this article, deep learning (DL) has attracted increasing attention for remaining useful life (RUL) prediction. However, most DL-based prognostics methods only provide deterministic RUL values while ignoring the associated epistemic and aleatoric ...
Yan-Hui Lin, Gang Li
semanticscholar   +1 more source

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities

Neural Information Processing Systems
Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. In particular, uncertainty can be used to improve the trustworthiness of LLMs by detecting factually incorrect model ...
Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen   +3 more
semanticscholar   +1 more source

LUQ: Long-text Uncertainty Quantification for LLMs

Conference on Empirical Methods in Natural Language Processing
Large Language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model’s confidence
Caiqi Zhang, Fangyu Liu, Marco Basaldella, Nigel Collier   +3 more
semanticscholar   +1 more source

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

ACM Computing Surveys
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society.
O. Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren, Anirudha Majumdar   +4 more
semanticscholar   +1 more source

Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction

Health Data Science
Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined to ...
Yingzhou Lu, Tianyi Chen, Nan Hao, Capucine Van Rechem, Jintai Chen, Tianfan Fu   +5 more
semanticscholar   +1 more source