{"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/17","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":17,"pages_in_order":24,"rows_per_page":100,"rows":[1601,1700],"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/16","next":"/task/uncertainty-quantification/papers/18","papers":[{"url":null,"slug":"sharing-information-between-machine-tools-to","title":"Sharing Information Between Machine Tools to Improve Surface Finish Forecasting","date":"2023-10-09","arxiv_id":"2310.05807","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-active-learning-via-dependent","title":"Improved Active Learning via Dependent Leverage Score Sampling","date":"2023-10-08","arxiv_id":"2310.04966","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypersindy-deep-generative-modeling-of","title":"HyperSINDy: Deep Generative Modeling of Nonlinear Stochastic Governing Equations","date":"2023-10-07","arxiv_id":"2310.04832","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-deep-learning-for-time-series-data","title":"Sparse Deep Learning for Time Series Data: Theory and Applications","date":"2023-10-05","arxiv_id":"2310.03243","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-deep-learning","title":"Uncertainty quantification for deep learning-based schemes for solving high-dimensional backward stochastic differential equations","date":"2023-10-05","arxiv_id":"2310.03393","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessment-of-prediction-intervals-using","title":"Assessment of Prediction Intervals Using Uncertainty Characteristics Curves","date":"2023-10-04","arxiv_id":"2310.03158","repositories_listed":0,"syntology":null},{"url":null,"slug":"eluquant-event-level-uncertainty","title":"ELUQuant: Event-Level Uncertainty Quantification in Deep Inelastic Scattering","date":"2023-10-04","arxiv_id":"2310.02913","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-inverse-models","title":"Uncertainty Quantification in Inverse Models in Hydrology","date":"2023-10-03","arxiv_id":"2310.02193","repositories_listed":0,"syntology":null},{"url":null,"slug":"lora-ensembles-for-large-language-model-fine","title":"LoRA ensembles for large language model fine-tuning","date":"2023-09-29","arxiv_id":"2310.00035","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-disconnect-between-theory-and-practice","title":"On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective","date":"2023-09-29","arxiv_id":"2310.00137","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointwise-uncertainty-quantification-for","title":"Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior","date":"2023-09-29","arxiv_id":"2310.00097","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-eosinophil","title":"Uncertainty Quantification for Eosinophil Segmentation","date":"2023-09-28","arxiv_id":"2309.16536","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-robust-semantic-segmentation-uncv2023","title":"The Robust Semantic Segmentation UNCV2023 Challenge Results","date":"2023-09-27","arxiv_id":"2309.15478","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-efficient-and-trustworthy-ai-through","title":"Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design","date":"2023-09-27","arxiv_id":"2309.15942","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-via-neural","title":"Uncertainty Quantification via Neural Posterior Principal Components","date":"2023-09-27","arxiv_id":"2309.15533","repositories_listed":0,"syntology":null},{"url":null,"slug":"characterising-user-transfer-amid-industrial","title":"Characterising User Transfer Amid Industrial Resource Variation: A Bayesian Nonparametric Approach","date":"2023-09-25","arxiv_id":"2309.13949","repositories_listed":0,"syntology":null},{"url":null,"slug":"dpa-wno-a-gray-box-model-for-a-class-of","title":"DPA-WNO: A gray box model for a class of stochastic mechanics problem","date":"2023-09-24","arxiv_id":"2309.15128","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-driven-exploration-strategies-for","title":"Uncertainty-driven Exploration Strategies for Online Grasp Learning","date":"2023-09-21","arxiv_id":"2309.12038","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-scalable-estimation-of-epistemic","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","date":"2023-09-20","arxiv_id":"2309.10976","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-against-uncertainty","title":"Adversarial Attacks Against Uncertainty Quantification","date":"2023-09-19","arxiv_id":"2309.10586","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-fidelity-climate-model-parameterization","title":"Multi-fidelity climate model parameterization for better generalization and extrapolation","date":"2023-09-19","arxiv_id":"2309.10231","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-ai-uncertainty-quantification-to","title":"Using AI Uncertainty Quantification to Improve Human Decision-Making","date":"2023-09-19","arxiv_id":"2309.10852","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unifying-perspective-on-non-stationary","title":"A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes","date":"2023-09-18","arxiv_id":"2309.10068","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-information-calibrated-conformal","title":"Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge","date":"2023-09-18","arxiv_id":"2309.09593","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-of-autoencoder","title":"Uncertainty Quantification of Autoencoder-based Koopman Operator","date":"2023-09-18","arxiv_id":"2309.09419","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-analysis-of-internal-reliability","title":"A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo","date":"2023-09-17","arxiv_id":"2309.09379","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-model-based-gaussian-process","title":"Scalable Model-Based Gaussian Process Clustering","date":"2023-09-14","arxiv_id":"2309.07882","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-of-uncertain-thoughts-reasoning-for","title":"Tree of Uncertain Thoughts Reasoning for Large Language Models","date":"2023-09-14","arxiv_id":"2309.07694","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-for-learned-ista","title":"Uncertainty quantification for learned ISTA","date":"2023-09-14","arxiv_id":"2309.07982","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-bayesian-inference-of","title":"Physics-informed Bayesian inference of external potentials in classical density-functional theory","date":"2023-09-13","arxiv_id":"2309.07065","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-geoscience-meets-foundation-models","title":"When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System","date":"2023-09-13","arxiv_id":"2309.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-bayesian-optimal-experiments-for","title":"Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models","date":"2023-09-12","arxiv_id":"2309.06540","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-segmentation-with-belief","title":"Medical Image Segmentation with Belief Function Theory and Deep Learning","date":"2023-09-12","arxiv_id":"2309.05914","repositories_listed":0,"syntology":null},{"url":null,"slug":"promises-of-deep-kernel-learning-for-control","title":"Promises of Deep Kernel Learning for Control Synthesis","date":"2023-09-12","arxiv_id":"2309.06569","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-reinforcement-learning-via","title":"Physics-informed reinforcement learning via probabilistic co-adjustment functions","date":"2023-09-11","arxiv_id":"2309.05404","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-driven-bayesian-control-of-port","title":"Data-driven Bayesian Control of Port-Hamiltonian Systems","date":"2023-09-09","arxiv_id":"2309.04678","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-uncertainty-quantification-in-a","title":"Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics","date":"2023-09-08","arxiv_id":"2309.04057","repositories_listed":0,"syntology":null},{"url":"/paper/improving-open-information-extraction-with","slug":"improving-open-information-extraction-with","title":"Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty","date":"2023-09-07","arxiv_id":"2309.03433","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-active-subspaces-for-effective-and","title":"Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks","date":"2023-09-06","arxiv_id":"2309.03061","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-trustworthiness-in-ml-based-network","title":"Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification","date":"2023-09-05","arxiv_id":"2310.10655","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-informed-polynomial-chaos-expansions","title":"Physics-Informed Polynomial Chaos Expansions","date":"2023-09-04","arxiv_id":"2309.01697","repositories_listed":0,"syntology":null},{"url":null,"slug":"minimal-assumptions-for-optimal-serology","title":"Analysis of Diagnostics (Part I): Prevalence, Uncertainty Quantification, and Machine Learning","date":"2023-08-30","arxiv_id":"2309.00645","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-uncertainty-in-answers-from-any","title":"Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness","date":"2023-08-30","arxiv_id":"2308.16175","repositories_listed":0,"syntology":null},{"url":null,"slug":"inferences-on-mixing-probabilities-and","title":"Inferences on Mixing Probabilities and Ranking in Mixed-Membership Models","date":"2023-08-29","arxiv_id":"2308.14988","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributionally-robust-statistical","title":"Distributionally Robust Statistical Verification with Imprecise Neural Networks","date":"2023-08-28","arxiv_id":"2308.14815","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto-weighted-bayesian-physics-informed","title":"Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution","date":"2023-08-24","arxiv_id":"2308.12864","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-load-forecasting-with-reservoir","title":"Probabilistic load forecasting with Reservoir Computing","date":"2023-08-24","arxiv_id":"2308.12844","repositories_listed":0,"syntology":null},{"url":null,"slug":"anisotropic-hybrid-networks-for-liver-tumor","title":"Anisotropic Hybrid Networks for liver tumor segmentation with uncertainty quantification","date":"2023-08-23","arxiv_id":"2308.11969","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-density-propagation-continual","title":"Variational Density Propagation Continual Learning","date":"2023-08-22","arxiv_id":"2308.11801","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-evidential-learning-for-bayesian","title":"Deep Evidential Learning for Bayesian Quantile Regression","date":"2023-08-21","arxiv_id":"2308.10650","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyper-association-graph-matching-with","title":"Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling","date":"2023-08-20","arxiv_id":"2308.10320","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-uncertainty-quantification-of-spent","title":"Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks","date":"2023-08-16","arxiv_id":"2308.08391","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-structured-kernel-design-for-power-flow","title":"Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning","date":"2023-08-15","arxiv_id":"2308.07867","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-the-quality-of-neural-network","title":"Comparing the quality of neural network uncertainty estimates for classification problems","date":"2023-08-11","arxiv_id":"2308.05903","repositories_listed":0,"syntology":null},{"url":null,"slug":"target-detection-on-hyperspectral-images","title":"Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks","date":"2023-08-11","arxiv_id":"2308.06293","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-inference-with-reliable-uncertainty","title":"Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction","date":"2023-08-08","arxiv_id":"2308.04237","repositories_listed":0,"syntology":null},{"url":null,"slug":"psrflow-probabilistic-super-resolution-with","title":"PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data","date":"2023-08-08","arxiv_id":"2308.04605","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-safe-and-reliable-ai-systems-for","title":"Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing","date":"2023-08-06","arxiv_id":"2308.03176","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-ranking-inferences-based-on-general","title":"Spectral Ranking Inferences based on General Multiway Comparisons","date":"2023-08-05","arxiv_id":"2308.02918","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-development-of-an-uncertainty","title":"Towards the Development of an Uncertainty Quantification Protocol for the Natural Gas Industry","date":"2023-08-05","arxiv_id":"2308.02941","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-machine-learning-for-discovery","title":"Interpretable Machine Learning for Discovery: Statistical Challenges \\& Opportunities","date":"2023-08-02","arxiv_id":"2308.01475","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-and-improving-latent-density","title":"Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging","date":"2023-07-31","arxiv_id":"2307.16694","repositories_listed":0,"syntology":null},{"url":null,"slug":"current-methods-for-drug-property-prediction","title":"Current Methods for Drug Property Prediction in the Real World","date":"2023-07-25","arxiv_id":"2309.17161","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-free-generalized-fiducial-inference","title":"Model-free generalized fiducial inference","date":"2023-07-24","arxiv_id":"2307.12472","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-theoretic-analysis-of-test-data","title":"Information-theoretic Analysis of Test Data Sensitivity in Uncertainty","date":"2023-07-23","arxiv_id":"2307.12456","repositories_listed":0,"syntology":null},{"url":null,"slug":"framework-for-developing-quantitative-agent","title":"FREIDA: A Framework for developing quantitative agent based models based on qualitative expert knowledge","date":"2023-07-21","arxiv_id":"2308.00505","repositories_listed":0,"syntology":null},{"url":null,"slug":"evil-evidential-inference-learning-for","title":"EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation","date":"2023-07-18","arxiv_id":"2307.08988","repositories_listed":0,"syntology":null},{"url":null,"slug":"physics-based-reduced-order-modeling-for","title":"Physics-based Reduced Order Modeling for Uncertainty Quantification of Guided Wave Propagation using Bayesian Optimization","date":"2023-07-18","arxiv_id":"2307.09661","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-data-efficient","title":"Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review","date":"2023-07-16","arxiv_id":"2307.08024","repositories_listed":0,"syntology":null},{"url":null,"slug":"bivariate-deepkriging-for-large-scale-spatial","title":"Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields","date":"2023-07-16","arxiv_id":"2307.08038","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-orientation-distribution-fields-for","title":"Neural Orientation Distribution Fields for Estimation and Uncertainty Quantification in Diffusion MRI","date":"2023-07-16","arxiv_id":"2307.08138","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-quantifying","title":"A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models","date":"2023-07-12","arxiv_id":"2307.05946","repositories_listed":0,"syntology":null},{"url":null,"slug":"function-space-regularization-for-deep","title":"Function-Space Regularization for Deep Bayesian Classification","date":"2023-07-12","arxiv_id":"2307.06055","repositories_listed":0,"syntology":null},{"url":null,"slug":"functional-pca-and-deep-neural-networks-based","title":"Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data","date":"2023-07-10","arxiv_id":"2307.05592","repositories_listed":0,"syntology":null},{"url":null,"slug":"seismic-data-interpolation-based-on-denoising","title":"Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling","date":"2023-07-09","arxiv_id":"2307.04226","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-korhunen-loeve-regression-model","title":"Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling","date":"2023-07-05","arxiv_id":"2307.02572","repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-segmentation-of-brain-white-matter","title":"Direct segmentation of brain white matter tracts in diffusion MRI","date":"2023-07-05","arxiv_id":"2307.02223","repositories_listed":0,"syntology":null},{"url":null,"slug":"transgressing-the-boundaries-towards-a","title":"Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness","date":"2023-07-05","arxiv_id":"2307.02454","repositories_listed":0,"syntology":null},{"url":null,"slug":"last-layer-state-space-model-for","title":"Last layer state space model for representation learning and uncertainty quantification","date":"2023-07-04","arxiv_id":"2307.01566","repositories_listed":0,"syntology":null},{"url":null,"slug":"morse-neural-networks-for-uncertainty","title":"Morse Neural Networks for Uncertainty Quantification","date":"2023-07-02","arxiv_id":"2307.00667","repositories_listed":0,"syntology":null},{"url":null,"slug":"applied-bayesian-structural-health-monitoring","title":"Applied Bayesian Structural Health Monitoring: inclinometer data anomaly detection and forecasting","date":"2023-07-01","arxiv_id":"2307.00305","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-besov-priors-for-bayesian","title":"Spatiotemporal Besov Priors for Bayesian Inverse Problems","date":"2023-06-28","arxiv_id":"2306.16378","repositories_listed":0,"syntology":null},{"url":null,"slug":"utopia-universally-trainable-optimal","title":"UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation","date":"2023-06-28","arxiv_id":"2306.16549","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-machine-learning-architectures","title":"Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems","date":"2023-06-27","arxiv_id":"2306.15159","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-multi-fidelity-modelling-for-digital","title":"Enhanced multi-fidelity modelling for digital twin and uncertainty quantification","date":"2023-06-26","arxiv_id":"2306.14430","repositories_listed":0,"syntology":null},{"url":null,"slug":"seeds-emulation-of-weather-forecast-ensembles","title":"SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models","date":"2023-06-24","arxiv_id":"2306.14066","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-bayesian-federated","title":"Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings","date":"2023-06-13","arxiv_id":"2306.07959","repositories_listed":0,"syntology":null},{"url":null,"slug":"user-defined-event-sampling-and-uncertainty","title":"User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems","date":"2023-06-13","arxiv_id":"2306.07526","repositories_listed":0,"syntology":null},{"url":null,"slug":"conformal-prediction-for-federated","title":"Conformal Prediction for Federated Uncertainty Quantification Under Label Shift","date":"2023-06-08","arxiv_id":"2306.05131","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-whole-heart-electromechanical","title":"Real-time whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations","date":"2023-06-08","arxiv_id":"2306.05321","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-uncertainty-in-pet-image","title":"Estimating Uncertainty in PET Image Reconstruction via Deep Posterior Sampling","date":"2023-06-07","arxiv_id":"2306.04664","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-large-language-model-annotations-for","title":"Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models","date":"2023-06-07","arxiv_id":"2306.04746","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-in-natural-language-processing","title":"Uncertainty in Natural Language Processing: Sources, Quantification, and Applications","date":"2023-06-05","arxiv_id":"2306.04459","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributionally-robust-uncertainty","title":"Distributionally robust uncertainty quantification via data-driven stochastic optimal control","date":"2023-06-04","arxiv_id":"2306.02318","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-general-framework-for-uncertainty-1","title":"A General Framework for Uncertainty Quantification via Neural SDE-RNN","date":"2023-06-01","arxiv_id":"2306.01189","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-pho-rmula-for-improved-performance-of","title":"A New PHO-rmula for Improved Performance of Semi-Structured Networks","date":"2023-06-01","arxiv_id":"2306.00522","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-operator-learning-based-surrogate-models","title":"Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles","date":"2023-06-01","arxiv_id":"2306.00810","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-deep-learning-model-uncertainty","title":"Quantifying Deep Learning Model Uncertainty in Conformal Prediction","date":"2023-06-01","arxiv_id":"2306.00876","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-convex-bayesian-learning-via-stochastic","title":"Non-convex Bayesian Learning via Stochastic Gradient Markov Chain Monte Carlo","date":"2023-05-30","arxiv_id":"2305.19350","repositories_listed":0,"syntology":null},{"url":null,"slug":"acceleration-of-stochastic-gradient-descent","title":"Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality","date":"2023-05-28","arxiv_id":"2305.17665","repositories_listed":0,"syntology":null}],"record_sha256":"aba411d1b745d015cf1bf26f061b093c2d5bb7a5708e11f5da2437d1023906a3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}