Paper Citation Record · LEDGER

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction

As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2505.00259.

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

pith.paper-citation-record.v1
2505.00259 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:52:24.558444Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:12:00.253732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:09:41.092661Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
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unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e27ca68-c87d-47cf-bd92-9c8445012dec · outbound

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

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T04:52:24.381103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78baf8f2-07be-404f-bd99-8b819b3c0d62 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Training data-efficient image transformers & distillation through attention

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.337387Z

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Observation 5ec679aa-fde4-4c40-92be-ce3cf01e6529 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Swin transformer: Hierarchical vision transformer using shifted windows

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.323229Z

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Observation 95f32d39-6996-4ab7-aa0f-7df8bdd2972f · outbound

This paper cites Mask R-CNN.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Mask R-CNN

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.303797Z

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Observation 5a0aa6ba-61ed-40b9-82f3-7d17f7bae701 · outbound

This paper cites Dual-mode learning for multi-dataset x-ray security image detection.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Dual-mode learning for multi-dataset x-ray security image detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.286867Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2023ae31-545f-42b1-ad93-978ca08f018e · outbound

This paper cites Segmenter: Transformer for semantic segmentation.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Segmenter: Transformer for semantic segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.272537Z

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Observation d1c2539f-4f0e-42de-9eb2-9040cc83921b · outbound

This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Segformer: Simple and efficient design for semantic segmentation with transformers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.259458Z

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Observation f0bdfc52-f667-4b8c-9a19-550a717078be · outbound

This paper cites MobileNetv2: Inverted residuals and linear bottlenecks.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction MobileNetv2: Inverted residuals and linear bottlenecks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.245017Z

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Observation 01d438e2-0d9b-48f3-b1f9-67bce38a7d85 · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.230118Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7c492bcf-69ce-4807-add2-52596fdcfbd2 · outbound

This paper cites When sparse neural network meets label noise learning: A multistage learning framework.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction When sparse neural network meets label noise learning: A multistage learning framework

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.212354Z

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Observation f5f0b569-744c-4a48-b366-804be3bb63fc · outbound

This paper cites Knowledge distillation: A survey.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Knowledge distillation: A survey

Reference 11

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Observation 3967669d-f679-42c6-8d0d-b268c42bd16e · outbound

This paper cites Knowledge distillation meets label noise learning: Ambiguity-guided mutual label refinery.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Knowledge distillation meets label noise learning: Ambiguity-guided mutual label refinery

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.182754Z

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Observation cacc3f73-8ff6-46f8-9284-2e2257b4ce8a · outbound

This paper cites A White Paper on Neural Network Quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction A White Paper on Neural Network Quantization

Reference 13

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Observation 8ac7d36f-1295-486e-b5e3-5101ca4c4162 · outbound

This paper cites BRECQ: Pushing the limit of post-training quantization by block reconstruction.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction BRECQ: Pushing the limit of post-training quantization by block reconstruction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.169476Z

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Observation b68372bd-7cbe-4968-b040-21dc0a335b2f · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.153329Z

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Observation d78326d2-1716-4d9c-a847-65c416b30930 · outbound

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

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 16

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Source-reported events for the cited work

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Observation 017a9546-9b03-4e75-9d43-757b688f3a22 · outbound

This paper cites PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.139196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 456b77d8-bda3-4bca-9e95-78200a0e2124 · outbound

This paper cites Up or down? adaptive rounding for post-training quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Up or down? adaptive rounding for post-training quantization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.121640Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 031a4f9e-9828-4009-9c5f-fd2e315b64fc · outbound

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

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T04:52:24.454792Z

Source-reported events for the cited work

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Observation b42b5c23-55db-449e-8eb9-4bd126922165 · outbound

This paper cites Learned Step Size Quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Learned Step Size Quantization

Reference 20

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Observation 98fcd5e1-36f3-4187-b4e4-446d0afc7518 · outbound

This paper cites Overcoming oscillations in quantization-aware training.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Overcoming oscillations in quantization-aware training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.104729Z

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Observation 9552335a-7979-47a3-8b9b-ce371cc7eefe · outbound

This paper cites Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization, 2023.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.088848Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4723f731-3410-4257-8e7b-b76e08ba125d · outbound

This paper cites NoisyQuant: Noisy bias- enhanced post-training activation quantization for vision transformers.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction NoisyQuant: Noisy bias- enhanced post-training activation quantization for vision transformers

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.074493Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a3b08cdd-6eb3-4593-a567-24dd2e711765 · outbound

This paper cites Lightweight maize disease detection through post-training quantization with similarity preservation.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Lightweight maize disease detection through post-training quantization with similarity preservation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.058752Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4832a80d-f8c1-49a8-8d1d-a0297281d195 · outbound

This paper cites PD-Quant: Post-training quantization based on prediction difference metric.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction PD-Quant: Post-training quantization based on prediction difference metric

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.041765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eade6f03-36d2-4598-9a57-37c3bf07b20e · outbound

This paper cites Data-free quantization through weight equalization and bias correction.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Data-free quantization through weight equalization and bias correction

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.028128Z

Source-reported events for the cited work

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Observation 19f59496-8e3d-45ff-b351-93714df16c3b · outbound

This paper cites Towards mixed-precision quantization of neural networks via constrained optimization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Towards mixed-precision quantization of neural networks via constrained optimization

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:25.011965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 24241c0e-01c3-4fa5-9082-22cf71ada95f · outbound

This paper cites HAWQ: Hessian aware quantization of neural networks with mixed-precision.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction HAWQ: Hessian aware quantization of neural networks with mixed-precision

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.995281Z

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Observation c5735f17-46c3-42b4-8e45-03e7c7090420 · outbound

This paper cites HAWQ-v2: Hessian aware trace-weighted quantization of neural networks.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction HAWQ-v2: Hessian aware trace-weighted quantization of neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.980022Z

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Observation b7fd4dcb-e0ed-4193-98f1-f5dbf1ff7a8e · outbound

This paper cites APTQ: Attention-aware post- training mixed-precision quantization for large language models.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction APTQ: Attention-aware post- training mixed-precision quantization for large language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.960240Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e6ba4fa2-1c8d-47e3-870e-119287a11811 · outbound

This paper cites ImageNet classification with deep convolutional neural networks.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction ImageNet classification with deep convolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.942524Z

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Observation 66748328-28c7-4e29-8f0c-bbb6cfeba6b2 · outbound

This paper cites RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization

Reference 32

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Observation 9b515ee5-a6a0-4b4c-9394-1b6c27460767 · outbound

This paper cites Solving oscillation problem in post-training quantization through a theoretical perspective.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Solving oscillation problem in post-training quantization through a theoretical perspective

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.928680Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 12b0460c-691b-44ec-bfb2-d8f1a457b329 · outbound

This paper cites Genie: Show me the data for quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Genie: Show me the data for quantization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.912375Z

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Observation cda6a03b-a0c4-47a1-9788-b08ded5936e5 · outbound

This paper cites Repq-ViT: Scale reparameterization for post-training quantization of vision transformers.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Repq-ViT: Scale reparameterization for post-training quantization of vision transformers

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.897058Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 679e7265-738c-4155-a63e-faf6edc743e0 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction 3d shapenets: A deep representation for volumetric shapes

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.882651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c06f5081-b8b7-45a8-bbf5-ec76451d831c · outbound

This paper cites I&S-ViT: An inclusive & stable method for pushing the limit of post-training vits quantization.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction I&S-ViT: An inclusive & stable method for pushing the limit of post-training vits quantization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-16T04:52:24.529257Z

Source-reported events for the cited work

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Observation f73ea687-cb72-46ba-b9f6-90b51203c1f2 · outbound

This paper cites Deep residual learning for image recognition.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Deep residual learning for image recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.868462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ab305a89-2801-4eb2-bd7e-6704eb5b8fcf · outbound

This paper cites Designing network design spaces.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Designing network design spaces

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.853117Z

Source-reported events for the cited work

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Observation 44f4f8ed-5569-427d-886a-b39e7283c6a1 · outbound

This paper cites Mnasnet: Platform-aware neural architecture search for mobile.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Mnasnet: Platform-aware neural architecture search for mobile

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.833043Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e070bd5d-32ed-42df-9e65-d0811400ec2c · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.818854Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db5144f7-717d-4e14-a38d-6ee2a2b0798f · outbound

This paper cites Texq: Zero-shot network quantization with texture feature distribution calibration.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Texq: Zero-shot network quantization with texture feature distribution calibration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.804667Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c87dc7de-877f-406c-b0e0-bd647b123578 · outbound

This paper cites AdaLog: Post-training quantization for vision transformers with adaptive logarithm quantizer.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction AdaLog: Post-training quantization for vision transformers with adaptive logarithm quantizer

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.788921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e9c21708-c98b-4887-975a-c6cd30d21bb6 · outbound

This paper cites Towards accu- rate post-training quantization for vision transformer.

Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction Towards accu- rate post-training quantization for vision transformer

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:52:24.774297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 41e7f247-dc28-44a6-81f5-960ed6dae169 · inbound

ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers cites this paper.

ScalePredictor: Instance-aware Scale Learning for Accurate Quantization of Vision Transformers Pack-PTQ: Advancing Post-training Quantization of Neural Networks by Pack-wise Reconstruction

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:09:41.094518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.