Paper Citation Record · LEDGER

Quantization without Tears

As of 17 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2411.13918.

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

pith.paper-citation-record.v1
2411.13918 v4

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:49:40.143447Z

measured 57 of 57 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 2026-08-07T04:10:25.151364Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:10:28.390856Z

Reference resolution

56 of 56 outbound references displayed

verified exact0
verified fuzzy51
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 73945738-33fe-4b0c-993e-948b1fcd77bd · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Quantization without Tears Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T15:49:39.592753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d067ca5-c621-4549-928b-8fba97b635d2 · outbound

This paper cites Piqa: Reasoning about physical common- sense in natural language.

Quantization without Tears Piqa: Reasoning about physical common- sense in natural language

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:42.010566Z

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 c02ad96f-7f82-472e-ab01-261e46301050 · outbound

This paper cites Cascade R-CNN: Delv- ing into high quality object detection.

Quantization without Tears Cascade R-CNN: Delv- ing into high quality object detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.980960Z

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 28c9849c-67df-4faa-9b3d-34f4ab523687 · outbound

This paper cites End-to- end object detection with Transformers.

Quantization without Tears End-to- end object detection with Transformers

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.954050Z

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 19292817-6fea-4288-bc16-7b245df18699 · outbound

This paper cites Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers.

Quantization without Tears Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:49:39.632960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6909ea4-7a32-4419-b6a5-e4c85225c8a9 · outbound

This paper cites Boolq: Exploring the surprising difficulty of natural yes/no questions.

Quantization without Tears Boolq: Exploring the surprising difficulty of natural yes/no questions

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.911772Z

Source-reported events for the cited work

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Observation ae15384f-8e4b-4d90-8195-c255c7f04317 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Quantization without Tears Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:49:39.651069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a848d93c-31a1-46a9-af48-d67f4438b084 · outbound

This paper cites ImageNet: A large-scale hierarchical im- age database.

Quantization without Tears ImageNet: A large-scale hierarchical im- age database

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.879605Z

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 10924fcb-a42b-4949-84b7-57801285dd42 · outbound

This paper cites Svirschevski, Vage Egiazarian, et al.

Quantization without Tears Svirschevski, Vage Egiazarian, et al

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.855599Z

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 d8cc59e6-b051-4f06-a08a-f0924b7f12b5 · outbound

This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

Quantization without Tears BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.831089Z

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 27f424c3-ec49-4b37-840e-710c538b6745 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Quantization without Tears An image is worth 16x16 words: Transformers for image recognition at scale

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.804724Z

Source-reported events for the cited work

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Observation 6aa773f0-d069-455a-b5a4-2cff7574b421 · outbound

This paper cites The Llama 3 Herd of Models.

Quantization without Tears The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:49:39.695019Z

Source-reported events for the cited work

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Observation 4085d2bb-e409-495d-8060-bb9a424f29d0 · outbound

This paper cites Learned step size quantization.

Quantization without Tears Learned step size quantization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.781236Z

Source-reported events for the cited work

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Observation 77c12b5d-850d-46cc-85f0-63d772309bac · outbound

This paper cites GPTQ: Accurate post-training quantization for gener- ative pre-trained Transformers.

Quantization without Tears GPTQ: Accurate post-training quantization for gener- ative pre-trained Transformers

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.755579Z

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 4cdaaec1-599a-4ca3-80ae-c844bc087c54 · outbound

This paper cites A framework for few-shot language model evaluation, 2021.

Quantization without Tears A framework for few-shot language model evaluation, 2021

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.728932Z

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 35ad8732-24a5-4ed5-a17a-0e112ec21c07 · outbound

This paper cites an unresolved cited work.

Quantization without Tears Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:49:41.695944Z

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 2e5a3f3b-460d-4561-9dc2-2987a4cd4876 · outbound

This paper cites Deep residual learning for image recognition.

Quantization without Tears Deep residual learning for image recognition

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.670043Z

Source-reported events for the cited work

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Observation dc87fdf8-814c-414b-a9fb-77a10896e43b · outbound

This paper cites Mask R-CNN.

Quantization without Tears Mask R-CNN

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.646116Z

Source-reported events for the cited work

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Observation 3273df37-c97f-4c90-9cd2-d261a6f6c897 · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Quantization without Tears Channel pruning for accelerating very deep neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.615429Z

Source-reported events for the cited work

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Observation e2038510-f064-4c57-b28f-6bb981996792 · outbound

This paper cites Mea- suring massive multitask language understanding.

Quantization without Tears Mea- suring massive multitask language understanding

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.569569Z

Source-reported events for the cited work

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Observation af29d070-0a1d-4c33-87e7-f6df3064ade9 · outbound

This paper cites Net- work quantization with element-wise gradient scaling.

Quantization without Tears Net- work quantization with element-wise gradient scaling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.539255Z

Source-reported events for the cited work

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Observation efb3672d-555b-471c-851f-b8b239e07669 · outbound

This paper cites Pruning filters for efficient convnets.

Quantization without Tears Pruning filters for efficient convnets

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.507365Z

Source-reported events for the cited work

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Observation fcc013e9-770a-4c7f-a0a0-fbfafaa5d9c9 · outbound

This paper cites Fully quantized network for object de- tection.

Quantization without Tears Fully quantized network for object de- tection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.481955Z

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 6bccd14e-2bd0-4893-9bf7-e9405f16640e · outbound

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

Quantization without Tears BRECQ: Pushing the limit of post-training quantization by block reconstruc- tion

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.462452Z

Source-reported events for the cited work

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Observation d1cfdf03-87a3-4b61-a0dd-541e5af8fa91 · outbound

This paper cites Q-ViT: Accurate and fully quan- tized low-bit Vision Transformer.

Quantization without Tears Q-ViT: Accurate and fully quan- tized low-bit Vision Transformer

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.434950Z

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 0265f910-9934-4a7e-a338-ffa076301dc2 · outbound

This paper cites I-ViT: Integer-only quantization for efficient Vision Transformer inference.

Quantization without Tears I-ViT: Integer-only quantization for efficient Vision Transformer inference

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.410340Z

Source-reported events for the cited work

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Observation 590f19e0-f1bc-4d48-823c-22c500f479f2 · outbound

This paper cites RepQ-ViT: Scale reparameterization for post-training quan- tization of Vision Transformers.

Quantization without Tears RepQ-ViT: Scale reparameterization for post-training quan- tization of Vision Transformers

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.380820Z

Source-reported events for the cited work

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Observation f74808f9-9718-45da-90c4-a0b5bbb1f962 · outbound

This paper cites Pruning and quantization for deep neural network acceleration: A survey.

Quantization without Tears Pruning and quantization for deep neural network acceleration: A survey

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.350106Z

Source-reported events for the cited work

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Observation f8486d70-ac41-4d5a-9b16-d1567a9772eb · outbound

This paper cites AWQ: Activation- aware weight quantization for LLM compression and accel- eration.

Quantization without Tears AWQ: Activation- aware weight quantization for LLM compression and accel- eration

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.328426Z

Source-reported events for the cited work

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Observation 0abc154d-da65-4034-bf82-e74bd74adca4 · outbound

This paper cites Mi- crosoft COCO: Common objects in context.

Quantization without Tears Mi- crosoft COCO: Common objects in context

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.297661Z

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 1536fe6d-248a-4cf2-a854-549d3f84967b · outbound

This paper cites FQ-ViT: Post-training quantization for fully quantized Vision Transformer.

Quantization without Tears FQ-ViT: Post-training quantization for fully quantized Vision Transformer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.274574Z

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 7d8d3ea2-09b6-48fb-a861-7ee79a16f74e · outbound

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

Quantization without Tears PD-Quant: Post-training quantiza- tion based on prediction difference metric

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.239087Z

Source-reported events for the cited work

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Observation b6eafab9-91af-4d1b-ae7e-2de0738adb14 · outbound

This paper cites ReActNet: Towards precise binary neural net- work with generalized activation functions.

Quantization without Tears ReActNet: Towards precise binary neural net- work with generalized activation functions

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.207076Z

Source-reported events for the cited work

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Observation 29bdafd2-9457-48c2-ba1b-82bdcb842f15 · outbound

This paper cites Swin Transformer: Hi- erarchical Vision Transformer using shifted windows.

Quantization without Tears Swin Transformer: Hi- erarchical Vision Transformer using shifted windows

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.164789Z

Source-reported events for the cited work

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Observation abfe13d1-a8a4-466f-91aa-0d6eabc1cd7c · outbound

This paper cites Post-training quantization for Vision Trans- former.

Quantization without Tears Post-training quantization for Vision Trans- former

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.128019Z

Source-reported events for the cited work

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Observation fb3cea62-f0ce-47bc-a19b-d7243f87e183 · outbound

This paper cites Decoupled weight de- cay regularization.

Quantization without Tears Decoupled weight de- cay regularization

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.099351Z

Source-reported events for the cited work

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Observation e1c59a57-36c0-4b11-84cd-cc178dd4735a · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Quantization without Tears Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.061249Z

Source-reported events for the cited work

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Observation 81279b10-f12e-4c3e-8e72-f3985f6cab36 · outbound

This paper cites Instance-aware group quantization for Vision Transformers.

Quantization without Tears Instance-aware group quantization for Vision Transformers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:41.022027Z

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 f4244b5e-a89b-410b-ae40-115b26511b1d · outbound

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

Quantization without Tears Up or down? adap- tive rounding for post-training quantization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.981558Z

Source-reported events for the cited work

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Observation d5cd3ee1-f673-4e8b-bf4b-792753df6166 · outbound

This paper cites NVIDIA TensorRT, 2024.

Quantization without Tears NVIDIA TensorRT, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.942069Z

Source-reported events for the cited work

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Observation a3eeb262-afa2-4bf9-9257-d0a70fe2d800 · outbound

This paper cites Scalable diffusion models with Transformers.

Quantization without Tears Scalable diffusion models with Transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.900195Z

Source-reported events for the cited work

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Observation da82d68e-2cf2-42fa-a1cb-5f99d71d5bb5 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Quantization without Tears Learn- ing transferable visual models from natural language super- vision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.861870Z

Source-reported events for the cited work

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Observation 8ff70714-06f7-4c87-aa00-6f96490ba3b9 · outbound

This paper cites Explor- ing the limits of transfer learning with a unified text-to-text Transformer.

Quantization without Tears Explor- ing the limits of transfer learning with a unified text-to-text Transformer

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.819774Z

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 4f7b7d18-e77c-44cf-87b5-aca30f38bb61 · outbound

This paper cites Applied regression analysis: a research tool.

Quantization without Tears Applied regression analysis: a research tool

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.773836Z

Source-reported events for the cited work

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Observation 03b964b1-7917-431c-9993-7e6473846457 · outbound

This paper cites WinoGrande: an adversarial winograd schema challenge at scale.

Quantization without Tears WinoGrande: an adversarial winograd schema challenge at scale

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.744916Z

Source-reported events for the cited work

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Observation 5a99e663-93d4-4d1a-b01b-606d2a086fa6 · outbound

This paper cites Social IQa: Commonsense reasoning about social interactions.

Quantization without Tears Social IQa: Commonsense reasoning about social interactions

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.713267Z

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 4401ad21-4dc9-4383-8155-433953357a9f · outbound

This paper cites Enhancing post-training quantization calibration through contrastive learning.

Quantization without Tears Enhancing post-training quantization calibration through contrastive learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.689662Z

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 9f6a61e0-2ff6-4ff4-819c-7d48e7618c67 · outbound

This paper cites Pointer sentinel mixture models.

Quantization without Tears Pointer sentinel mixture models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.663530Z

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 e9d2e15e-4bcb-49bd-9557-11277f6bd73d · outbound

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

Quantization without Tears Training data-efficient image transformers & distillation through at- tention

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.637330Z

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 1a479163-c665-4281-97a3-c70272e682a4 · outbound

This paper cites Atten- tion is all you need.

Quantization without Tears Atten- tion is all you need

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.598025Z

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 d6f392b9-1e1d-4e62-bce4-2a334a6a3176 · outbound

This paper cites Distilling knowl- edge by mimicking features.

Quantization without Tears Distilling knowl- edge by mimicking features

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.565372Z

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 f90559c1-cbbb-4da6-9c3a-897f49dc26e2 · outbound

This paper cites QDROP: Randomly dropping quantization for extremely low-bit post-training quantization.

Quantization without Tears QDROP: Randomly dropping quantization for extremely low-bit post-training quantization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.529114Z

Source-reported events for the cited work

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Observation 2859a870-d35c-4a7b-9507-008cee2339c9 · outbound

This paper cites AdaLog: Post-training quantization for Vi- sion Transformers with adaptive logarithm quantizer.

Quantization without Tears AdaLog: Post-training quantization for Vi- sion Transformers with adaptive logarithm quantizer

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.496921Z

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 a373677d-91be-41dc-833f-f67e994f3082 · outbound

This paper cites PTQ4ViT: Post-training quantization for Vi- sion Transformers with twin uniform quantization.

Quantization without Tears PTQ4ViT: Post-training quantization for Vi- sion Transformers with twin uniform quantization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.453962Z

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 59ad91e5-e1d6-43a7-949e-c9f0c8116dcb · outbound

This paper cites HellaSwag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, page 4791–4800, 2019.

Quantization without Tears HellaSwag: Can a machine really finish your sentence? In Annual Meeting of the Association for Computational Linguistics, page 4791–4800, 2019

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.408038Z

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 a554ed42-7268-44e5-b6b6-80d4fdab5d35 · outbound

This paper cites Quantized feature distillation for network quantization.

Quantization without Tears Quantized feature distillation for network quantization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:49:40.377701Z

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 14fbf50e-08e3-4b08-a4f7-be4a85d06ead · inbound

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers cites this paper.

GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers Quantization without Tears

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:10:28.442548Z

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.