Paper Citation Record · LEDGER

Data-Augmented Quantization-Aware Knowledge Distillation

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2509.03850.

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

pith.paper-citation-record.v1
2509.03850 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:41:58.089296Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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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

43 of 43 outbound references displayed

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verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fde5ba9f-88d1-43fb-8e17-c99d611d9e4b · outbound

This paper cites Stochastic precision ensemble: Self-knowledge distil- lation for quantized deep neural networks.

Data-Augmented Quantization-Aware Knowledge Distillation Stochastic precision ensemble: Self-knowledge distil- lation for quantized deep neural networks

Reference 1

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Observation ea37e7e7-2f02-4c9f-bd09-a7dd85357908 · outbound

This paper cites On the efficacy of knowledge distillation.

Data-Augmented Quantization-Aware Knowledge Distillation On the efficacy of knowledge distillation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.876301Z

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Observation 42074f30-7de9-4c5f-8c4e-874f285a72f3 · outbound

This paper cites Pact: Parameterized clipping activation for quantized neural networks, 2018.

Data-Augmented Quantization-Aware Knowledge Distillation Pact: Parameterized clipping activation for quantized neural networks, 2018

Reference 3

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Observation b6bbaf9d-48f1-429a-8230-10f0b3990dd0 · outbound

This paper cites Binarized neural networks: Train- ing deep neural networks with weights and activations con- strained to +1 or -1, 2016.

Data-Augmented Quantization-Aware Knowledge Distillation Binarized neural networks: Train- ing deep neural networks with weights and activations con- strained to +1 or -1, 2016

Reference 4

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Observation 840784c2-4fec-4f72-af2a-07a6048cd63d · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

Data-Augmented Quantization-Aware Knowledge Distillation AutoAugment: Learning Augmentation Policies from Data

Reference 5

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Observation 8162a07c-b660-4f12-bfd7-2555b1eb22d1 · outbound

This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Data-Augmented Quantization-Aware Knowledge Distillation Randaugment: Practical automated data augmentation with a reduced search space

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.844753Z

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Observation a74d35de-748d-4129-b841-54261d88542e · outbound

This paper cites Esser, Jeffrey L.

Data-Augmented Quantization-Aware Knowledge Distillation Esser, Jeffrey L

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.833278Z

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Observation c9303048-3a0c-4fb8-9b58-2264016cbd38 · outbound

This paper cites A survey of quan- tization methods for efficient neural network inference.

Data-Augmented Quantization-Aware Knowledge Distillation A survey of quan- tization methods for efficient neural network inference

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.821518Z

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Observation 7c94671e-87de-4cbd-8770-ecf42fb2f14b · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Data-Augmented Quantization-Aware Knowledge Distillation AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 9

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Observation e3f04e81-7c3e-48ba-8684-aab7210871e7 · outbound

This paper cites Distilling the knowledge in a neural network, 2015.

Data-Augmented Quantization-Aware Knowledge Distillation Distilling the knowledge in a neural network, 2015

Reference 10

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Source-reported events for the cited work

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Observation 50482bca-63cd-40a0-b316-b012eb3292d0 · outbound

This paper cites Population based augmentation: Efficient learning of aug- mentation policy schedules.

Data-Augmented Quantization-Aware Knowledge Distillation Population based augmentation: Efficient learning of aug- mentation policy schedules

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.801220Z

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Observation 9369ac46-a14f-43b8-a433-4c66c319be3b · outbound

This paper cites Relation networks for object detection.

Data-Augmented Quantization-Aware Knowledge Distillation Relation networks for object detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.791659Z

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Observation 6f870779-2161-4ca3-b4e3-741ecff8c164 · outbound

This paper cites Like what you like: Knowl- edge distill via neuron selectivity transfer, 2017.

Data-Augmented Quantization-Aware Knowledge Distillation Like what you like: Knowl- edge distill via neuron selectivity transfer, 2017

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.781661Z

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Observation 980d3bec-cb19-4d75-8ff0-0fc3303b145c · outbound

This paper cites Layercam: Exploring hierarchical class activation maps.

Data-Augmented Quantization-Aware Knowledge Distillation Layercam: Exploring hierarchical class activation maps

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.772142Z

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Observation f81403c1-6321-4e3c-b1d5-8f7d665c8bc6 · outbound

This paper cites Learning to quantize deep networks by optimizing quantization intervals with task loss, 2018.

Data-Augmented Quantization-Aware Knowledge Distillation Learning to quantize deep networks by optimizing quantization intervals with task loss, 2018

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.762779Z

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Observation 16006ac9-d6a1-4c64-9e07-beeda53b1be1 · outbound

This paper cites Qkd: Quantization-aware knowledge distillation,.

Data-Augmented Quantization-Aware Knowledge Distillation Qkd: Quantization-aware knowledge distillation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.753139Z

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Observation e087cd4e-178e-47be-b3ff-3a021d8ca43e · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper, 2018.

Data-Augmented Quantization-Aware Knowledge Distillation Quantizing deep convolutional networks for efficient inference: A whitepaper, 2018

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.743221Z

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Observation 7a347335-274c-4737-8e30-1abb8ab8b815 · outbound

This paper cites Learning multiple layers of features from tiny images.(2009), 2009.

Data-Augmented Quantization-Aware Knowledge Distillation Learning multiple layers of features from tiny images.(2009), 2009

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.733585Z

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Observation 15966359-1686-4918-963a-7242409c1db0 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Data-Augmented Quantization-Aware Knowledge Distillation Tiny imagenet visual recognition challenge

Reference 19

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Observation 64a98c7b-f6cc-42fc-aaef-bb79da52f877 · outbound

This paper cites Network quantization with element-wise gradient scaling, 2021.

Data-Augmented Quantization-Aware Knowledge Distillation Network quantization with element-wise gradient scaling, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.620333Z

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Observation 88b0c18d-9fee-4f3d-9c92-e6a9c541795c · outbound

This paper cites Mqbench: Towards reproducible and deployable model quan- tization benchmark, 2022.

Data-Augmented Quantization-Aware Knowledge Distillation Mqbench: Towards reproducible and deployable model quan- tization benchmark, 2022

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.416857Z

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Observation a457b1e4-69ed-4142-a02d-ffb4cee3e49f · outbound

This paper cites Fast autoaugment, 2019.

Data-Augmented Quantization-Aware Knowledge Distillation Fast autoaugment, 2019

Reference 22

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Observation 77ab273a-fc76-4e96-9910-7998d6d29efe · outbound

This paper cites Trivialaugment: Tuning- free yet state-of-the-art data augmentation.

Data-Augmented Quantization-Aware Knowledge Distillation Trivialaugment: Tuning- free yet state-of-the-art data augmentation

Reference 23

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Observation 29663c4d-d639-49c4-9715-11924ffcf54f · outbound

This paper cites Re- lational knowledge distillation.

Data-Augmented Quantization-Aware Knowledge Distillation Re- lational knowledge distillation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:01.329163Z

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Observation 860e0767-bff0-4d58-bd3d-a9ac7204767e · outbound

This paper cites Correla- tion congruence for knowledge distillation.

Data-Augmented Quantization-Aware Knowledge Distillation Correla- tion congruence for knowledge distillation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:01.012160Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 405a172e-44b9-4ae2-92a2-51b4b37705c4 · outbound

This paper cites Correlation congruence for knowledge distillation, 2019.

Data-Augmented Quantization-Aware Knowledge Distillation Correlation congruence for knowledge distillation, 2019

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:00.858029Z

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Observation 2bcd2d40-49b0-4b7f-b196-ec5ba10951e4 · outbound

This paper cites Collabo- rative multi-teacher knowledge distillation for learning low bit-width deep neural networks.

Data-Augmented Quantization-Aware Knowledge Distillation Collabo- rative multi-teacher knowledge distillation for learning low bit-width deep neural networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:00.687544Z

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Observation b83fc938-5432-46b7-93b2-f8b33d5e32e7 · outbound

This paper cites Xnor-net: Imagenet classification using binary convolutional neural networks, 2016.

Data-Augmented Quantization-Aware Knowledge Distillation Xnor-net: Imagenet classification using binary convolutional neural networks, 2016

Reference 28

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Observation c2590acf-5402-4d60-97e1-608a0041ddd7 · outbound

This paper cites Berg, and Li Fei-Fei.

Data-Augmented Quantization-Aware Knowledge Distillation Berg, and Li Fei-Fei

Reference 29

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Source-reported events for the cited work

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Observation e7bcfe11-9243-412d-b49f-321cabecbd4a · outbound

This paper cites Teachaugment: Data augmentation optimiza- tion using teacher knowledge.

Data-Augmented Quantization-Aware Knowledge Distillation Teachaugment: Data augmentation optimiza- tion using teacher knowledge

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:00.381547Z

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Observation 8e2750bb-3a7f-4c55-88c5-441b39ecdbee · outbound

This paper cites Going deeper with convolutions.

Data-Augmented Quantization-Aware Knowledge Distillation Going deeper with convolutions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:00.241356Z

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Observation 35e121e2-7637-4eb4-80e6-8874a78ab009 · outbound

This paper cites Contrastive representation distillation, 2022.

Data-Augmented Quantization-Aware Knowledge Distillation Contrastive representation distillation, 2022

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:00.068976Z

Source-reported events for the cited work

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Observation 1bf79ed7-11f7-41c6-88ed-3d832395c121 · outbound

This paper cites Similarity-preserving knowl- edge distillation, 2019.

Data-Augmented Quantization-Aware Knowledge Distillation Similarity-preserving knowl- edge distillation, 2019

Reference 33

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 67b65f06-b4c5-40ef-858f-67eea1845338 · outbound

This paper cites What makes a ”good” data augmentation in knowledge distillation - a statistical perspective.

Data-Augmented Quantization-Aware Knowledge Distillation What makes a ”good” data augmentation in knowledge distillation - a statistical perspective

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:59.760402Z

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Observation 9953107e-d347-4fe3-a117-82aad7353885 · outbound

This paper cites What makes a” good” data augmentation in knowledge distillation- a statistical perspective.

Data-Augmented Quantization-Aware Knowledge Distillation What makes a” good” data augmentation in knowledge distillation- a statistical perspective

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:59.638160Z

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Observation a574d6be-320a-4f7f-8fe9-b014c745a303 · outbound

This paper cites Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information.

Data-Augmented Quantization-Aware Knowledge Distillation Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information

Reference 36

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Observation 833b6ae1-94c9-4a1b-9382-d3e568e3a6e3 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features, 2019.

Data-Augmented Quantization-Aware Knowledge Distillation Cutmix: Regu- larization strategy to train strong classifiers with localizable features, 2019

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:59.505445Z

Source-reported events for the cited work

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Observation 52cfdc6a-b8ba-424b-802c-b8a7e4612485 · outbound

This paper cites Paying more atten- tion to attention: Improving the performance of convolutional neural networks via attention transfer, 2017.

Data-Augmented Quantization-Aware Knowledge Distillation Paying more atten- tion to attention: Improving the performance of convolutional neural networks via attention transfer, 2017

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:59.339560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 491e7e0c-a8d3-4e95-b6c6-12e423321c97 · outbound

This paper cites Dauphin, and David Lopez-Paz.

Data-Augmented Quantization-Aware Knowledge Distillation Dauphin, and David Lopez-Paz

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:59.140089Z

Source-reported events for the cited work

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Observation 07ca6933-dbf4-4bcc-9ca3-50d8b6fff220 · outbound

This paper cites Adversarial autoaugment, 2019.

Data-Augmented Quantization-Aware Knowledge Distillation Adversarial autoaugment, 2019

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:58.965747Z

Source-reported events for the cited work

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Observation e23c2e99-e39a-497a-8a7f-d078f5f50e4d · outbound

This paper cites Self-supervised quantization- aware knowledge distillation.

Data-Augmented Quantization-Aware Knowledge Distillation Self-supervised quantization- aware knowledge distillation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:58.816824Z

Source-reported events for the cited work

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Observation 579a18ee-7ba4-4379-867a-3169edda4b9a · outbound

This paper cites Dorefa-net: Training low bitwidth convolu- tional neural networks with low bitwidth gradients, 2018.

Data-Augmented Quantization-Aware Knowledge Distillation Dorefa-net: Training low bitwidth convolu- tional neural networks with low bitwidth gradients, 2018

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:58.622774Z

Source-reported events for the cited work

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Observation 5ff04a04-43a7-4124-879c-2a11b96c1660 · outbound

This paper cites Towards effective low-bitwidth convolutional neural networks.

Data-Augmented Quantization-Aware Knowledge Distillation Towards effective low-bitwidth convolutional neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:41:58.371337Z

Source-reported events for the cited work

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Pith citing papers

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