Paper Citation Record · LEDGER

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression

As of 18 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.03012.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.03012 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:17:50.456958Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact6
verified fuzzy5
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba260054-35fd-4987-9a3f-96e491ba7a52 · outbound

This paper cites Calibration of Model Uncertainty for Dropout Variational Inference.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Calibration of Model Uncertainty for Dropout Variational Inference

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.608288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2804b8ff-7434-4a07-bf25-e20fdaf894b4 · outbound

This paper cites DEJA VU: Continual Model Generalization For Unseen Domains.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression DEJA VU: Continual Model Generalization For Unseen Domains

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.589323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b5db2161-2d12-4290-a826-4e0c65da89d1 · outbound

This paper cites Neural Unsupervised Domain Adaptation in NLP---A Survey.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Neural Unsupervised Domain Adaptation in NLP---A Survey

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.570792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ade6282d-ea36-4020-b9a3-b37a61540d66 · outbound

This paper cites HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.533116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 76af75b6-86f5-4ec9-9738-ecb6c172d0ec · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T11:17:50.449339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef39eef8-178d-4699-8252-f4f5da54d2b5 · outbound

This paper cites Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.497319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d37c006e-64b2-4856-9411-24b02fbda841 · outbound

This paper cites Domain adaptation with structural correspondence learning.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Domain adaptation with structural correspondence learning

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:17:50.721108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b0ff6ab-addb-42b0-9bdd-74f0c3bca112 · outbound

This paper cites Deep ordinal regres- sion network for monocular depth estimation.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Deep ordinal regres- sion network for monocular depth estimation

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:17:50.707291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06cd630d-8360-48f2-9692-2ca6282993fc · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Adam: A Method for Stochastic Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-05T11:17:50.410910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2336795d-aa43-4708-b5ea-134c19f88a85 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-05T11:17:50.415876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f6d298a-ad06-4474-a6c0-e0f78d7a2c4c · outbound

This paper cites Learning to combine: Knowledge aggregation for multi-source domain adaptation.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Learning to combine: Knowledge aggregation for multi-source domain adaptation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:17:50.655445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1ad19bed-5104-4456-86bb-5520093478f5 · outbound

This paper cites USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-05T11:17:50.552754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2d727229-bbcf-49a1-b534-f794ad35e26d · outbound

This paper cites Online domain adaptation for semantic segmentation in ever-changing conditions.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Online domain adaptation for semantic segmentation in ever-changing conditions

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:17:50.684793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c081a611-e5e9-49cf-a89f-6f5f19108d47 · outbound

This paper cites Domain-adversarial training of neural networks.The journal of machine learning research, 17(1):2096–2030,.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Domain-adversarial training of neural networks.The journal of machine learning research, 17(1):2096–2030,

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T11:17:50.406660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 662bc5b2-499b-46e9-8842-d7b7814eaefb · outbound

This paper cites Monocular depth estimation in new environments with absolute scale.

Uncertainty-aware Test-Time Training (UT$^3$) for Efficient On-the-fly Domain Adaptive Dense Regression Monocular depth estimation in new environments with absolute scale

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:17:50.670166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.