{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/uncertainty-quantification/papers/12","list_of":"/task/uncertainty-quantification","task":"Uncertainty Quantification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":12,"pages_in_order":24,"rows_per_page":100,"rows":[1101,1200],"of":2366,"counts":{"archive_papers_tagged":2366,"with_a_code_link":832,"where_syntology_ran_a_sample":220,"not_listed_spam_title":0,"listed":2366,"listed_where_code_ran":220,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":179,"every_run_a_failure_of_syntologys_instrument":41,"listed_with_a_run_with_no_instrument_failure":179,"listed_every_run_a_failure_of_syntologys_instrument":41,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/uncertainty-quantification","prev":"/task/uncertainty-quantification/papers/11","next":"/task/uncertainty-quantification/papers/13","papers":[{"url":"/paper/federated-generalised-variational-inference-a","slug":"federated-generalised-variational-inference-a","title":"Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework","date":"2025-02-02","arxiv_id":"2502.00846","repositories_listed":0,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/federated-generalised-variational-inference-a#ran","syntology_url":"https://syntology.ai/paper/2502.00846","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00846"}},"official":null}},{"url":null,"slug":"muti-fidelity-prediction-and-uncertainty","title":"Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations","date":"2025-02-01","arxiv_id":"2502.00550","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-of-wind-gust","title":"Uncertainty Quantification of Wind Gust Predictions in the Northeast US: An Evidential Neural Network and Explainable Artificial Intelligence Approach","date":"2025-02-01","arxiv_id":"2502.00300","repositories_listed":0,"syntology":null},{"url":null,"slug":"position-curvature-matrices-should-be","title":"Position: Curvature Matrices Should Be Democratized via Linear Operators","date":"2025-01-31","arxiv_id":"2501.19183","repositories_listed":0,"syntology":null},{"url":"/paper/redefining-machine-unlearning-a-conformal","slug":"redefining-machine-unlearning-a-conformal","title":"Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach","date":"2025-01-31","arxiv_id":"2501.19403","repositories_listed":0,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/redefining-machine-unlearning-a-conformal#ran","syntology_url":"https://syntology.ai/paper/2501.19403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.19403"}},"official":null}},{"url":null,"slug":"trustworthy-evaluation-of-generative-ai","title":"Trustworthy Evaluation of Generative AI Models","date":"2025-01-31","arxiv_id":"2501.18897","repositories_listed":0,"syntology":null},{"url":null,"slug":"barnn-a-bayesian-autoregressive-and-recurrent","title":"BARNN: A Bayesian Autoregressive and Recurrent Neural Network","date":"2025-01-30","arxiv_id":"2501.18665","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-joint-recovery-method-for-co-2","title":"Probabilistic Joint Recovery Method for CO$_2$ Plume Monitoring","date":"2025-01-30","arxiv_id":"2501.18761","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-online-conformal-prediction-under","title":"Robust Online Conformal Prediction under Uniform Label Noise","date":"2025-01-30","arxiv_id":"2501.18363","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-image-to-image-translation","title":"Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios","date":"2025-01-29","arxiv_id":"2501.17570","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidential-physics-informed-neural-networks","title":"Evidential Physics-Informed Neural Networks","date":"2025-01-27","arxiv_id":"2501.15908","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-modelling-of-biological-systems","title":"Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century","date":"2025-01-22","arxiv_id":"2501.13142","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-with-noise","title":"Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective","date":"2025-01-21","arxiv_id":"2501.12314","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-generative-models-fair-a-study-of-racial","title":"Are generative models fair? A study of racial bias in dermatological image generation","date":"2025-01-20","arxiv_id":"2501.11752","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-bayesian-neural-networks-make-confident","title":"Can Bayesian Neural Networks Make Confident Predictions?","date":"2025-01-20","arxiv_id":"2501.11773","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-operator-networks-for-bayesian-parameter","title":"Deep Operator Networks for Bayesian Parameter Estimation in PDEs","date":"2025-01-18","arxiv_id":"2501.10684","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-diagnostic-in-3d-covid-19-pneumonia","title":"Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification","date":"2025-01-18","arxiv_id":"2501.10770","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-recursive-bayesian-neural-network-for","title":"A recursive Bayesian neural network for constitutive modeling of sands under monotonic loading","date":"2025-01-17","arxiv_id":"2501.10088","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-digital-twins-robust-model","title":"Uncertainty-Aware Digital Twins: Robust Model Predictive Control using Time-Series Deep Quantile Learning","date":"2025-01-17","arxiv_id":"2501.10337","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-bayesian-physics-informed-kolmogorov","title":"Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks","date":"2025-01-15","arxiv_id":"2501.08501","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-synthesis-of-uncertainty","title":"A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation","date":"2025-01-14","arxiv_id":"2501.08188","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-human-hand-segmentation-on-in","title":"Testing Human-Hand Segmentation on In-Distribution and Out-of-Distribution Data in Human-Robot Interactions Using a Deep Ensemble Model","date":"2025-01-13","arxiv_id":"2501.07713","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-online-extrinsic","title":"Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction Approach","date":"2025-01-12","arxiv_id":"2501.06878","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-calibration-via-conformalized","title":"Distilling Calibration via Conformalized Credal Inference","date":"2025-01-10","arxiv_id":"2501.06066","repositories_listed":0,"syntology":null},{"url":null,"slug":"geophysical-inverse-problems-with-measurement","title":"Geophysical inverse problems with measurement-guided diffusion models","date":"2025-01-08","arxiv_id":"2501.04881","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-skip-connections-for","title":"Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks","date":"2025-01-08","arxiv_id":"2501.04816","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-uncertainty-quantification-for","title":"Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks","date":"2025-01-08","arxiv_id":"2501.04234","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-soft-sensor-method-with-uncertainty","title":"A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval","date":"2025-01-06","arxiv_id":"2501.03295","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-aleatoric-to-epistemic-exploring","title":"From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence","date":"2025-01-05","arxiv_id":"2501.03282","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-computational-framework-for","title":"A Systematic Computational Framework for Practical Identifiability Analysis in Mathematical Models Arising from Biology","date":"2025-01-02","arxiv_id":"2501.01283","repositories_listed":0,"syntology":null},{"url":null,"slug":"marketing-mix-modeling-in-lemonade","title":"Marketing Mix Modeling in Lemonade","date":"2025-01-02","arxiv_id":"2501.01276","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ai-powered-bayesian-generative-modeling","title":"An AI-powered Bayesian generative modeling approach for causal inference in observational studies","date":"2025-01-01","arxiv_id":"2501.00755","repositories_listed":0,"syntology":null},{"url":null,"slug":"monty-hall-and-optimized-conformal-prediction","title":"Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs","date":"2024-12-31","arxiv_id":"2501.00555","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-out-of-distribution","title":"Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes","date":"2024-12-30","arxiv_id":"2412.20918","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-interval-construction-and","title":"Confidence Interval Construction and Conditional Variance Estimation with Dense ReLU Networks","date":"2024-12-29","arxiv_id":"2412.20355","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-the-whole-is-greater-than-the-sum-of-its-1","title":"When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquer","date":"2024-12-29","arxiv_id":"2412.20323","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-improving","title":"Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction","date":"2024-12-27","arxiv_id":"2412.19511","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-stereo-matching","title":"Uncertainty Quantification in Stereo Matching","date":"2024-12-24","arxiv_id":"2412.18703","repositories_listed":0,"syntology":null},{"url":null,"slug":"condensed-stein-variational-gradient-descent","title":"Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks","date":"2024-12-21","arxiv_id":"2412.16462","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-continual-open","title":"Uncertainty Quantification in Continual Open-World Learning","date":"2024-12-21","arxiv_id":"2412.16409","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliability-analysis-for-non-deterministic","title":"Reliability analysis for non-deterministic limit-states using stochastic emulators","date":"2024-12-18","arxiv_id":"2412.13731","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-separation-via-ensemble-quantile","title":"Uncertainty separation via ensemble quantile regression","date":"2024-12-18","arxiv_id":"2412.13738","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-free-uncertainty-quantification-2","title":"Distribution-Free Uncertainty Quantification in Mechanical Ventilation Treatment: A Conformal Deep Q-Learning Framework","date":"2024-12-17","arxiv_id":"2412.12597","repositories_listed":0,"syntology":null},{"url":null,"slug":"ba-bfl-barycentric-aggregation-for-bayesian","title":"Information-Geometric Barycenters for Bayesian Federated Learning","date":"2024-12-16","arxiv_id":"2412.11646","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-breast-cancer-molecular-subtype","title":"Reliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammography","date":"2024-12-16","arxiv_id":"2412.11953","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-staged-deep-learning-approach-to-spatial","title":"A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport","date":"2024-12-14","arxiv_id":"2412.10945","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-generative-and-physics-based","title":"Integrating Generative and Physics-Based Models for Ptychographic Imaging with Uncertainty Quantification","date":"2024-12-14","arxiv_id":"2412.10882","repositories_listed":0,"syntology":null},{"url":null,"slug":"prescribing-decision-conservativeness-in-two","title":"Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach","date":"2024-12-13","arxiv_id":"2412.10554","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-reweighting-on-the-predictive-role-of","title":"Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization","date":"2024-12-12","arxiv_id":"2412.08869","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-free-uncertainty-quantification-1","title":"Distribution free uncertainty quantification in neuroscience-inspired deep operators","date":"2024-12-12","arxiv_id":"2412.09369","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-tracking-for-online-system","title":"Subspace tracking for online system identification","date":"2024-12-12","arxiv_id":"2412.09052","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimized-deep-ensemble-for","title":"Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling","date":"2024-12-11","arxiv_id":"2412.08776","repositories_listed":0,"syntology":null},{"url":null,"slug":"cups-improving-human-pose-shape-estimators","title":"CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty","date":"2024-12-11","arxiv_id":"2412.10431","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-uncertainty-quantification-of","title":"Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage","date":"2024-12-10","arxiv_id":"2412.07075","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-random-fields-and-their-application-to","title":"Dual Random Fields and their Application to Mineral Potential Mapping","date":"2024-12-10","arxiv_id":"2412.07488","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-confidence-aware-uncertainty-estimation","title":"Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation","date":"2024-12-10","arxiv_id":"2412.07255","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-certain-are-uncertainty-estimates-three","title":"How Certain are Uncertainty Estimates? Three Novel Earth Observation Datasets for Benchmarking Uncertainty Quantification in Machine Learning","date":"2024-12-09","arxiv_id":"2412.06451","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-uncertainty-quantification-of","title":"A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions","date":"2024-12-07","arxiv_id":"2412.05563","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-powered-digital-twin-of-the-ocean-reliable","title":"AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble","date":"2024-12-07","arxiv_id":"2412.05475","repositories_listed":0,"syntology":null},{"url":null,"slug":"dawn-si-data-aware-and-noise-informed","title":"DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems","date":"2024-12-06","arxiv_id":"2412.04766","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hidden-physics-and-system-parameters","title":"Learning Hidden Physics and System Parameters with Deep Operator Networks","date":"2024-12-06","arxiv_id":"2412.05133","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-transformer","title":"Uncertainty Quantification for Transformer Models for Dark-Pattern Detection","date":"2024-12-06","arxiv_id":"2412.05251","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-priors-for-satellite-image-restoration","title":"Deep priors for satellite image restoration with accurate uncertainties","date":"2024-12-05","arxiv_id":"2412.04130","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-portfolio","title":"Uncertainty Quantification in Portfolio Temperature Alignment","date":"2024-12-05","arxiv_id":"2412.14182","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-aware-classification-via-uncertainty","title":"Risk-aware Classification via Uncertainty Quantification","date":"2024-12-04","arxiv_id":"2412.03391","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-and-quantifying","title":"Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation","date":"2024-12-04","arxiv_id":"2412.03178","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-trust-in-large-language-models-with","title":"Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning","date":"2024-12-03","arxiv_id":"2412.02904","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-reliable-predictions-in-detection","title":"Identifying Reliable Predictions in Detection Transformers","date":"2024-12-02","arxiv_id":"2412.01782","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-conformal-inference-through-localized","title":"Spatial Conformal Inference through Localized Quantile Regression","date":"2024-12-02","arxiv_id":"2412.01098","repositories_listed":0,"syntology":null},{"url":null,"slug":"take-your-steps-hierarchically-efficient","title":"Towards Clinical Practice in CT-Based Pulmonary Disease Screening: An Efficient and Reliable Framework","date":"2024-12-02","arxiv_id":"2412.01525","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-artificial-intelligence-for","title":"Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives","date":"2024-12-02","arxiv_id":"2412.01272","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-inference-with-fast-feature","title":"Predictive Inference With Fast Feature Conformal Prediction","date":"2024-12-01","arxiv_id":"2412.00653","repositories_listed":0,"syntology":null},{"url":null,"slug":"autopq-automating-quantile-estimation-from","title":"AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability","date":"2024-11-30","arxiv_id":"2412.00419","repositories_listed":0,"syntology":null},{"url":null,"slug":"per-event-uncertainty-quantification-for-flow","title":"Per-event Uncertainty Quantification for Flow Cytometry using Calibration Beads","date":"2024-11-28","arxiv_id":"2411.19191","repositories_listed":0,"syntology":null},{"url":null,"slug":"redesigning-the-ensemble-kalman-filter-with-a","title":"Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty","date":"2024-11-28","arxiv_id":"2411.18864","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-size-and-shape-functional-mixed","title":"Probabilistic size-and-shape functional mixed models","date":"2024-11-27","arxiv_id":"2411.18416","repositories_listed":0,"syntology":null},{"url":null,"slug":"lc-svd-dlinear-a-low-cost-physics-based","title":"LC-SVD-DLinear: A low-cost physics-based hybrid machine learning model for data forecasting using sparse measurements","date":"2024-11-26","arxiv_id":"2411.17433","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-white-matter","title":"Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification","date":"2024-11-26","arxiv_id":"2411.17571","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-of-bayesian-uncertainty","title":"A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation","date":"2024-11-25","arxiv_id":"2411.16370","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-in-hospital-mortality-prediction","title":"Enhancing In-Hospital Mortality Prediction Using Multi-Representational Learning with LLM-Generated Expert Summaries","date":"2024-11-25","arxiv_id":"2411.16818","repositories_listed":0,"syntology":null},{"url":null,"slug":"epinet-for-content-cold-start","title":"Epinet for Content Cold Start","date":"2024-11-20","arxiv_id":"2412.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-spatio-temporal-uncertainty","title":"Hierarchical Spatio-Temporal Uncertainty Quantification for Distributed Energy Adoption","date":"2024-11-19","arxiv_id":"2411.12193","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivariate-and-online-transfer-learning","title":"Multivariate and Online Transfer Learning with Uncertainty Quantification","date":"2024-11-19","arxiv_id":"2411.12555","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-uncertainty-quantification-via","title":"Fine-Grained Uncertainty Quantification via Collisions","date":"2024-11-18","arxiv_id":"2411.12127","repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-foundations-of-conformal","title":"Theoretical Foundations of Conformal Prediction","date":"2024-11-18","arxiv_id":"2411.11824","repositories_listed":0,"syntology":null},{"url":null,"slug":"melanoma-detection-with-uncertainty","title":"Melanoma Detection with Uncertainty Quantification","date":"2024-11-15","arxiv_id":"2411.10322","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-in-supply-chain-digital-twins-a","title":"Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach","date":"2024-11-15","arxiv_id":"2411.10254","repositories_listed":0,"syntology":null},{"url":null,"slug":"counterfactual-uncertainty-quantification-of","title":"Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials","date":"2024-11-14","arxiv_id":"2411.09635","repositories_listed":0,"syntology":null},{"url":null,"slug":"inherently-interpretable-and-uncertainty","title":"Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems","date":"2024-11-14","arxiv_id":"2411.09393","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidential-time-to-event-prediction-model","title":"Evidential time-to-event prediction with calibrated uncertainty quantification","date":"2024-11-12","arxiv_id":"2411.07853","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-gradient-optimization-for-field","title":"Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac","date":"2024-11-11","arxiv_id":"2411.07018","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-enabled-velocity-model","title":"Machine learning-enabled velocity model building with uncertainty quantification","date":"2024-11-11","arxiv_id":"2411.06651","repositories_listed":0,"syntology":null},{"url":null,"slug":"uq-of-2d-slab-burner-dns-surrogates","title":"UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration","date":"2024-11-09","arxiv_id":"2411.16693","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-forecasting-of-the-dynamics-of-a","title":"Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decomposition","date":"2024-11-08","arxiv_id":"2411.07263","repositories_listed":0,"syntology":null},{"url":null,"slug":"game-theoretic-defenses-for-robust-conformal","title":"Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging","date":"2024-11-07","arxiv_id":"2411.04376","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-prediction-neural-network-upnet","title":"Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing Retrieval","date":"2024-11-07","arxiv_id":"2411.04556","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-clinical","title":"Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models","date":"2024-11-05","arxiv_id":"2411.03497","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-uncertainty-in-llms-to-enhance","title":"Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI","date":"2024-11-04","arxiv_id":"2411.02381","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-uncertainty-in-3d-gaussian-splatting","title":"Modeling Uncertainty in 3D Gaussian Splatting through Continuous Semantic Splatting","date":"2024-11-04","arxiv_id":"2411.02547","repositories_listed":0,"syntology":null},{"url":null,"slug":"targeted-learning-for-variable-importance","title":"Targeted Learning for Variable Importance","date":"2024-11-04","arxiv_id":"2411.02221","repositories_listed":0,"syntology":null}],"record_sha256":"3bd4ff519639207744bdc62da8c94678518a2d422e6bcf23198839eb7493c6c3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}