Paper Citation Record · LEDGER

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation

As of 22 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2412.09899.

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

pith.paper-citation-record.v1
2412.09899 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:41:15.836016Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

16 of 16 outbound references displayed

verified exact1
verified fuzzy2
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bcd9a49e-2c70-412e-bb1f-15c5e3e4fcc5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.785787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0fec398-c4f6-42ff-b185-5b502f602cc0 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.797015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4faaf0f9-449f-45b6-8036-bc9747d5346d · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.803969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb7b798-ade8-4e09-8cb2-0441d9bd6a3b · outbound

This paper cites RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation RepQ-ViT: Scale Reparameterization for Post-Training Quantization of Vision Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.807388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c47bfa2b-355c-4d8a-af26-c35699194443 · outbound

This paper cites A White Paper on Neural Network Quantization.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation A White Paper on Neural Network Quantization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.814934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31d510a8-3fe6-4c41-bc74-903edeacae72 · outbound

This paper cites Towards Stable Test-Time Adaptation in Dynamic Wild World.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Towards Stable Test-Time Adaptation in Dynamic Wild World

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.818253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e46cc99-f79c-4778-9aa8-2c15505c049c · outbound

This paper cites Test-time unsupervised domain adaptation.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Test-time unsupervised domain adaptation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:16.001770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c366aab6-59cb-4666-932f-ae3eec547b8a · outbound

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

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.828526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 741e6bc6-9e27-41a6-85a4-df51f618678c · outbound

This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.832524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a1922a1-8694-4bc9-8531-6292c4a71e50 · outbound

This paper cites Jun Shi, Jianfeng Xu, Kazuyuki Tasaka, and Zhibo Chen.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Jun Shi, Jianfeng Xu, Kazuyuki Tasaka, and Zhibo Chen

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:41:16.012761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 87531768-9827-4563-81bb-8a6a787a96cf · outbound

This paper cites IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation IDa-Det: An Information Discrepancy-aware Distillation for 1-bit Detectors

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:41:15.870174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c203d5de-693c-4e5b-b0d8-b7344afdcc7b · outbound

This paper cites Visual Prompt Tuning for Test-time Domain Adaptation.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Visual Prompt Tuning for Test-time Domain Adaptation

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.793216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dbb6216-c17f-4eaf-9139-c60181dfa2ce · outbound

This paper cites Learned Step Size Quantization.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Learned Step Size Quantization

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.789476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81bace61-f86f-40a3-8cd3-c4125582440e · outbound

This paper cites Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.800486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56572403-d4ca-4df8-b540-72bce5f2cbe6 · outbound

This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.811044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a086033-99e3-405d-9fc9-3dbd19ffd31d · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T16:41:15.781285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.