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Paper Citation Record · LEDGER

Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2004.09602.

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

pith.paper-citation-record.v1
2004.09602 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:37.533756Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

218
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1b7ac546-f2c6-47a8-94d8-29d51490788e · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 170

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arxiv_id, observed 2026-05-13T13:35:36.181856Z

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.

source=arxiv_source observed=2026-05-13T13:35:35.972596Z digest=sha256:3d3251742aa12ffb11acd6bffd22e7a9f22c76634f199937fee869497af873f9

Observation 7f7950c7-18ea-4467-ab28-d3572da8b7d6 · inbound

FP8 Formats for Deep Learning cites this paper.

FP8 Formats for Deep Learning Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 23

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arxiv_id, observed 2026-05-15T09:47:03.823240Z

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

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Observation 6987a822-5131-46eb-8a78-4b2cbcec28c5 · inbound

Adaptive Semantic Token Communication for Transformer-based Edge Inference cites this paper.

Adaptive Semantic Token Communication for Transformer-based Edge Inference Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 30

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Observation ba2b57fa-d859-4d6e-8514-009769030acd · inbound

Power-of-Two (PoT) Weights in Large Language Models (LLMs) cites this paper.

Power-of-Two (PoT) Weights in Large Language Models (LLMs) Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 5

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no resolver link, observed 2026-08-07T12:12:12.442972Z

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Observation 97168a3a-9f63-4c09-88df-0f4a53ed8a5d · inbound

Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition cites this paper.

Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 46

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no resolver link, observed 2026-08-07T11:48:04.806412Z

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source=arxiv_source observed=2026-08-07T11:48:04.806412Z digest=sha256:ee01c00f20beecda3922b3a7a4f3ac55d9fccaed7c3dda386c16b27e27257b35

Observation d3259b1b-281b-46b4-8d6f-76daf87549d5 · inbound

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models cites this paper.

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 37

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Observation b3b3f99f-06db-4ab7-a298-b7d592d8ec7a · inbound

Compress Any Segment Anything Model (SAM) cites this paper.

Compress Any Segment Anything Model (SAM) Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 22

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no resolver link, observed 2026-08-06T18:20:01.054268Z

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source=pdf_text observed=2026-08-06T18:20:01.054268Z digest=sha256:629c9d2acf07748711bc7521e4611f2b6f03595bafdcf30f131f06ca01c60175

Observation cdc6778d-2a6f-4b35-86d9-374984c81022 · inbound

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization cites this paper.

DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 64

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source=pdf_text observed=2026-08-06T16:42:12.199052Z digest=sha256:e276c9f5c23fe90f92ec191507223e390b62c6323ba2c39e9ab181a2814c5fea

Observation 45840cbb-72ee-4c4f-8e37-e9f5d9e50cfb · inbound

FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models cites this paper.

FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 29

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source=pdf_text observed=2026-08-06T05:40:49.038808Z digest=sha256:998ce210629c25d7eeae787d5d5d3e8cd33f8cf923130989e04a5df8b688222b

Observation 70b0a9f2-3e07-4c69-b13c-7988d25889fd · inbound

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 171

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no resolver link, observed 2026-08-05T22:14:25.968694Z

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source=pdf_text observed=2026-08-05T22:14:25.968694Z digest=sha256:cad1c6cb6f61bf3f799df3962ab90fa79b568ca307f392fe775d8b6bd1e022eb

Observation 5a4bf14f-38f9-4456-be4f-c72fc3b6e620 · inbound

Float8@2bits: Entropy Coding Enables Data-Free Model Compression cites this paper.

Float8@2bits: Entropy Coding Enables Data-Free Model Compression Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 2020

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no resolver link, observed 2026-08-03T06:31:25.776256Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:31:25.776256Z digest=sha256:a6daff1b430aeb374f2e84b3b7ce193fe6d81e8d56051ca0f28a0fc6a3d8efd9

Observation cfcbfe30-b875-445e-85a6-b7bf9690cc15 · inbound

DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines cites this paper.

DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 56

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arxiv_id, observed 2026-05-10T13:20:25.559806Z

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.

source=pdf_text observed=2026-05-10T13:19:07.809807Z digest=sha256:5b25923815b42a89ff427ec1090176f8e4a711205ed8bfd1461d7b82067c4e48

Observation ef43d0be-b3b7-4f94-a423-64dbf4425ecb · inbound

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy cites this paper.

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 40

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arxiv_id, observed 2026-05-11T20:41:09.361664Z

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.

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Observation b11c958c-c8e7-41db-be04-b9420632ae17 · inbound

Quantamination: Dynamic Quantization Leaks Your Data Across the Batch cites this paper.

Quantamination: Dynamic Quantization Leaks Your Data Across the Batch Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 11

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arxiv_id, observed 2026-05-12T09:26:26.126828Z

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.

source=pdf_text observed=2026-05-07T10:54:55.555926Z digest=sha256:b58a9bcc7c82b8ec3c951dbef450ad8b8e954b00e1740ac3b5d55aca6758c694

Observation 298aa269-ad3f-4f13-b0ef-4502da03ce3b · inbound

Edge AI for Automotive Vulnerable Road User Safety: Deployable Detection via Knowledge Distillation cites this paper.

Edge AI for Automotive Vulnerable Road User Safety: Deployable Detection via Knowledge Distillation Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 11

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arxiv_id, observed 2026-05-12T09:36:25.997306Z

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

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Observation d5c3725f-3028-447b-afa4-a2894fe57f4f · inbound

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization cites this paper.

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 6

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verified exact
arxiv_id, observed 2026-05-13T07:52:31.102099Z

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

source=arxiv_source observed=2026-05-13T07:47:38.066716Z digest=sha256:c6af29fc000f1e95aee5ac30bcd5e1f10eada1aeef02cb0cae8452d6940b6f0b

Observation 92293adb-0d91-4667-9e23-3c8a73a98f9e · inbound

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks cites this paper.

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 55

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arxiv_id, observed 2026-05-22T06:36:10.290668Z

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

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Observation 75f74f80-d2c1-4287-a2da-7561e066b15b · inbound

Transformers Provably Learn to Internalize Chain-of-Thought cites this paper.

Transformers Provably Learn to Internalize Chain-of-Thought Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 51

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arxiv_id, observed 2026-06-29T14:33:30.544786Z

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.

source=arxiv_source observed=2026-06-29T14:29:10.010212Z digest=sha256:01a7e30747ac6f1b9aa46212f7badd4124776f28c0006f4ef1567d93dbbe129c

Observation e7c9e099-038b-4cdc-81b4-76f93b3a840d · inbound

Learning through Internalization cites this paper.

Learning through Internalization Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 28

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arxiv_id, observed 2026-07-04T03:39:31.028930Z

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

source=arxiv_source observed=2026-06-26T17:43:18.915404Z digest=sha256:14b7cb503be153cfd56e3a4525ec1d1800951818d7eeac60539edf733a2d52fd

Observation 15487451-e522-402f-8682-57d9ced1c672 · inbound

Jack of All Scales: A Versatile FPGA Tensor Block for MXFP Precisions cites this paper.

Jack of All Scales: A Versatile FPGA Tensor Block for MXFP Precisions Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 4

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no resolver link, observed 2026-08-02T03:25:29.313595Z

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Observation 3c3cbe49-3751-4064-9ac4-7c526d9d93fa · inbound

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks cites this paper.

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 2018

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source=pdf_text observed=2026-08-02T06:57:39.362250Z digest=sha256:8fa730e00d1aa0dfae23b94532446cb85941c923b8119681081d6b3aa1bd8b37

Observation 31666098-4755-48b3-9dea-d1477f05a35e · inbound

INT8 Quantization Makes ARM Edge Inference Dispatch-Invariant cites this paper.

INT8 Quantization Makes ARM Edge Inference Dispatch-Invariant Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 28

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no resolver link, observed 2026-08-01T00:06:42.126247Z

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source=pdf_text observed=2026-08-01T00:06:42.126247Z digest=sha256:7ec53de177d4a58d2ed0a07e32e7e920d0804e3fc001409db093fb943833daf6

Observation 9fb0fd25-fcb7-4af6-8c2b-9bf7f723f62f · inbound

Approximate reservoir computing with a semiconductor laser for reducing energy consumption cites this paper.

Approximate reservoir computing with a semiconductor laser for reducing energy consumption Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 15

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no resolver link, observed 2026-07-31T23:58:12.963140Z

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source=pdf_text observed=2026-07-31T23:58:12.963140Z digest=sha256:bf0b36d502bb25a8b83a0a788f4b1b4b61c1162f33e9e833412b9c64d7e4e789