Paper Citation Record · LEDGER

Knowledge distillation for optimization of quantized deep neural networks

As of 18 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:1909.01688.

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

pith.paper-citation-record.v1
1909.01688 v3

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:12:37.318893Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:12:37.213837Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T05:12:37.432021Z

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy21
unresolved6
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19b8e24f-d24a-4a8a-a649-be3b20ca24a1 · outbound

This paper cites Quantization is a widely used compression technique, and even 1- or 2-bit models can show quite good performance.

Knowledge distillation for optimization of quantized deep neural networks Quantization is a widely used compression technique, and even 1- or 2-bit models can show quite good performance

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.679574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4a858944-93f7-425d-a60a-970c0c05feb5 · outbound

This paper cites Knowledge distillation for optimization of quantized deep neural networks.

Knowledge distillation for optimization of quantized deep neural networks Knowledge distillation for optimization of quantized deep neural networks

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T05:12:37.436811Z

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Observation df3cd24f-3c1d-4229-884f-b4815e4eb04e · outbound

This paper cites Experimental setup Dataset: We employ CIFAR-10 and CIFAR-100 datasets for exper- iments.

Knowledge distillation for optimization of quantized deep neural networks Experimental setup Dataset: We employ CIFAR-10 and CIFAR-100 datasets for exper- iments

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.667821Z

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Observation ac285446-d026-42e6-80a3-5710ac70f01e · outbound

This paper cites We found that the teacher needs not be a quantized neural network.

Knowledge distillation for optimization of quantized deep neural networks We found that the teacher needs not be a quantized neural network

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.655817Z

Source-reported events for the cited work

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Observation 1483d901-e558-4f02-846c-f6a7e3924140 · outbound

This paper cites Quantized neural networks: Training neural networks with low precision weights and ac- tivations.,.

Knowledge distillation for optimization of quantized deep neural networks Quantized neural networks: Training neural networks with low precision weights and ac- tivations.,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.645043Z

Source-reported events for the cited work

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Observation b2a6def2-2478-479c-82e5-83466b0933d0 · outbound

This paper cites Fixed-point feedfor- ward deep neural network design using weights +1, 0, and -1,.

Knowledge distillation for optimization of quantized deep neural networks Fixed-point feedfor- ward deep neural network design using weights +1, 0, and -1,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.633634Z

Source-reported events for the cited work

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Observation e6582dd7-5ef6-480d-89b6-d94e4295fe68 · outbound

This paper cites Alternating multi- bit quantization for recurrent neural networks,.

Knowledge distillation for optimization of quantized deep neural networks Alternating multi- bit quantization for recurrent neural networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.621850Z

Source-reported events for the cited work

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Observation ad2eef77-e339-4207-a3f5-520b03a76d73 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Knowledge distillation for optimization of quantized deep neural networks DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 8

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Source-reported events for the cited work

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Observation 80a8b3e4-5aad-4412-a152-6c281fcca069 · outbound

This paper cites Balanced quantization: An effective and efﬁcient approach to quantized neural networks,.

Knowledge distillation for optimization of quantized deep neural networks Balanced quantization: An effective and efﬁcient approach to quantized neural networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.611308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eda6dc65-2e85-48fc-acf8-ae91902c5e64 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Knowledge distillation for optimization of quantized deep neural networks Distilling the Knowledge in a Neural Network

Reference 10

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Source-reported events for the cited work

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Observation 3ac977e6-2d0e-48c8-ab4a-d8131cdc2e2c · outbound

This paper cites Model compression,.

Knowledge distillation for optimization of quantized deep neural networks Model compression,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.601308Z

Source-reported events for the cited work

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Observation db1862d4-3784-47e4-b456-5e57425977b1 · outbound

This paper cites Recurrent neural network training with dark knowledge transfer,.

Knowledge distillation for optimization of quantized deep neural networks Recurrent neural network training with dark knowledge transfer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.589846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc0b1068-f4fd-46d5-9dd5-c563b95abe11 · outbound

This paper cites Neural compatibility modeling with atten- tive knowledge distillation,.

Knowledge distillation for optimization of quantized deep neural networks Neural compatibility modeling with atten- tive knowledge distillation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.579738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b9b2941-66e5-4a71-b7bd-3617c0e486a9 · outbound

This paper cites Domain adaptation of dnn acoustic models using knowledge distillation,.

Knowledge distillation for optimization of quantized deep neural networks Domain adaptation of dnn acoustic models using knowledge distillation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.569240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b9fec52d-c881-4cb3-a6b3-136a9cc6b0e1 · outbound

This paper cites Deepvid: Deep visual interpretation and diagnosis for image classiﬁers via knowledge distillation,.

Knowledge distillation for optimization of quantized deep neural networks Deepvid: Deep visual interpretation and diagnosis for image classiﬁers via knowledge distillation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.557548Z

Source-reported events for the cited work

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Observation 9563c1b3-4a27-4a74-86e1-60db9d27ea4c · outbound

This paper cites FitNets: Hints for Thin Deep Nets.

Knowledge distillation for optimization of quantized deep neural networks FitNets: Hints for Thin Deep Nets

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T05:12:37.269519Z

Source-reported events for the cited work

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Observation bce7662a-ed3b-49c6-a1cb-ebdeb7990dea · outbound

This paper cites Knowledge distillation using unlabeled mismatched images.

Knowledge distillation for optimization of quantized deep neural networks Knowledge distillation using unlabeled mismatched images

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-14T05:12:37.384507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5028761d-d9b1-40e8-b907-4b4a9d6e6a3e · outbound

This paper cites Re- lational knowledge distillation,.

Knowledge distillation for optimization of quantized deep neural networks Re- lational knowledge distillation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.546630Z

Source-reported events for the cited work

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Observation c31c1537-53d9-4bec-a25c-ea8ebced3dc3 · outbound

This paper cites A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,.

Knowledge distillation for optimization of quantized deep neural networks A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.534582Z

Source-reported events for the cited work

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Observation 847f7734-92bd-4afc-977e-06dea9453aac · outbound

This paper cites Apprentice: Using knowledge distillation techniques to improve low-precision network accu- racy,.

Knowledge distillation for optimization of quantized deep neural networks Apprentice: Using knowledge distillation techniques to improve low-precision network accu- racy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.522638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 19341e68-5796-4777-a03a-c7e89bbc5b04 · outbound

This paper cites Model compression via distillation and quantization,.

Knowledge distillation for optimization of quantized deep neural networks Model compression via distillation and quantization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.510428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef73ebd9-c02d-461a-9222-9e2eb9178e24 · outbound

This paper cites Improved Knowledge Distillation via Teacher Assistant.

Knowledge distillation for optimization of quantized deep neural networks Improved Knowledge Distillation via Teacher Assistant

Reference 22

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Source-reported events for the cited work

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Observation b86212fe-b6a5-41fb-9b93-3690a9ca48d8 · outbound

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

Knowledge distillation for optimization of quantized deep neural networks Xnor-net: Imagenet classiﬁcation using binary convolutional neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.498256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba6eb2da-ce01-4bb7-817f-ef7d927837cf · outbound

This paper cites Fixed-point optimization of deep neural networks with adaptive step size retraining,.

Knowledge distillation for optimization of quantized deep neural networks Fixed-point optimization of deep neural networks with adaptive step size retraining,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.485460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6d9b9527-4058-4828-b418-7bb1ffa719d0 · outbound

This paper cites Resiliency of Deep Neural Networks under Quantization.

Knowledge distillation for optimization of quantized deep neural networks Resiliency of Deep Neural Networks under Quantization

Reference 25

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Source-reported events for the cited work

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Observation a35c978f-6bae-4789-9ea6-61804f03519a · outbound

This paper cites Wide Residual Networks.

Knowledge distillation for optimization of quantized deep neural networks Wide Residual Networks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T05:12:37.307773Z

Source-reported events for the cited work

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Observation c86db4fe-d3dd-4db9-81ae-12ed13805818 · outbound

This paper cites Deep residual learning for image recognition,.

Knowledge distillation for optimization of quantized deep neural networks Deep residual learning for image recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.473672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e373b972-d2c1-43ab-969a-b0de0dc45403 · outbound

This paper cites Memoriza- tion capacity of deep neural networks under parameter quanti- zation,.

Knowledge distillation for optimization of quantized deep neural networks Memoriza- tion capacity of deep neural networks under parameter quanti- zation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.462069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 36252f86-26de-4c6e-bcc1-db014a49a260 · outbound

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

Knowledge distillation for optimization of quantized deep neural networks Towards effective low-bitwidth convolutional neural networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:12:37.449795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 4a858944-93f7-425d-a60a-970c0c05feb5 · inbound