Paper Citation Record · LEDGER

Low-bit Model Quantization for Deep Neural Networks: A Survey

As of 18 August 2026, this Paper Citation Record lists 100 of 115 outbound references and 8 inbound Pith citation observations for arXiv:2505.05530.

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

pith.paper-citation-record.v1
2505.05530 v1

Coverage vector

measured 100 of 115 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:13:19.861392Z

measured 108 of 108 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T11:46:50.520083Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:45:48.761436Z

Reference resolution

100 of 115 outbound references displayed

verified exact0
verified fuzzy46
unresolved54
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4c433dc-e64d-4bc7-8dc8-955244e2d58d · outbound

This paper cites Pointer sentinel mixture models,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Pointer sentinel mixture models,

Reference 1

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Observation 7c9f85dd-55e2-4cbc-b044-6ecf5af51e1d · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Exploring the limits of transfer learning with a unified text-to-text transformer,

Reference 2

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Observation ad231187-c68c-4996-bcd4-223664066619 · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Low-bit Model Quantization for Deep Neural Networks: A Survey Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 3

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Observation de0fffe7-7431-4478-bcd4-a6bc94ac23b3 · outbound

This paper cites Stanford alpaca: An instruction- following llama model,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Stanford alpaca: An instruction- following llama model,

Reference 4

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Observation 7c8247db-e210-48d4-bced-65a5883417c7 · outbound

This paper cites The flan collection: Designing data and methods for effective instruction tuning,.

Low-bit Model Quantization for Deep Neural Networks: A Survey The flan collection: Designing data and methods for effective instruction tuning,

Reference 5

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Observation 0b3dec0d-62e1-48f5-aad9-f71b897218ac · outbound

This paper cites Squad: 100,000+ questions for machine comprehension of text,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Squad: 100,000+ questions for machine comprehension of text,

Reference 6

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Observation ccd6baba-56b0-4b14-845a-8a3a7ccc11ee · outbound

This paper cites The penn treebank: annotating predicate argument structure,.

Low-bit Model Quantization for Deep Neural Networks: A Survey The penn treebank: annotating predicate argument structure,

Reference 7

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Observation 08cb68cb-d43c-495e-88ff-37b35be21364 · outbound

This paper cites Glue: A multi-task benchmark and analysis platform for natural language understanding,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Glue: A multi-task benchmark and analysis platform for natural language understanding,

Reference 8

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Observation 6fe00d19-7168-4563-bb3a-87f0cb87066d · outbound

This paper cites Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks,

Reference 9

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Observation 16d7adfe-f2f4-4780-ad5b-3618f7971f4d · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Piqa: Reasoning about physical commonsense in natural language,

Reference 10

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Observation 84168dd0-f48a-41ac-858c-aa153cd7b27f · outbound

This paper cites Hellaswag: Can a machine really finish your sentence?.

Low-bit Model Quantization for Deep Neural Networks: A Survey Hellaswag: Can a machine really finish your sentence?

Reference 11

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Observation b0ac00a0-d44f-4331-830a-12e3afaaccbc · outbound

This paper cites Wino- grande: An adversarial winograd schema challenge at scale,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Wino- grande: An adversarial winograd schema challenge at scale,

Reference 12

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Observation 1c37cafe-eab3-4842-adaa-16e302cbae4f · outbound

This paper cites Attention is all you need,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Attention is all you need,

Reference 13

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Observation 22d826d9-34f1-4800-83f5-b4cc49affddc · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Low-bit Model Quantization for Deep Neural Networks: A Survey OPT: Open Pre-trained Transformer Language Models

Reference 14

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Observation dec6f935-5ee6-4d0f-91a8-a93cbdb3e141 · outbound

This paper cites BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

Low-bit Model Quantization for Deep Neural Networks: A Survey BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Reference 15

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Observation ca5d9ce6-7ad0-4b55-9359-7c3af10f133a · outbound

This paper cites Glm-130b: An open bilingual pre-trained model,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Glm-130b: An open bilingual pre-trained model,

Reference 16

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Observation 6bc31284-9388-4808-95ec-b520ae9a210e · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Low-bit Model Quantization for Deep Neural Networks: A Survey Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 17

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Observation 3cdf1d09-e01d-4e0b-a0b9-5677059a34f5 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Low-bit Model Quantization for Deep Neural Networks: A Survey Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 18

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Observation df12727a-21cb-46ab-98a4-79ea8d457b2c · outbound

This paper cites The Falcon Series of Open Language Models.

Low-bit Model Quantization for Deep Neural Networks: A Survey The Falcon Series of Open Language Models

Reference 19

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Observation 3672858d-5088-413b-885b-4370345dedfd · outbound

This paper cites Mixtral of Experts.

Low-bit Model Quantization for Deep Neural Networks: A Survey Mixtral of Experts

Reference 20

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Observation e6babd0f-a2c7-47de-b7f5-7125fe98c1f3 · outbound

This paper cites Bert: Pre- training of deep bidirectional transformers for language under- standing,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Bert: Pre- training of deep bidirectional transformers for language under- standing,

Reference 21

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Observation 374d54f2-233d-4f67-ab84-6d7a2fa89782 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Low-bit Model Quantization for Deep Neural Networks: A Survey RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 22

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Observation f7ea281b-ef24-4523-bc03-cee510a8d9d2 · outbound

This paper cites Xlnet: Generalized autoregressive pretraining for language understanding,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Xlnet: Generalized autoregressive pretraining for language understanding,

Reference 23

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Observation c7376160-75d2-4927-957c-6695b45996b5 · outbound

This paper cites Gpt-j-6b: A 6 billion parameter autoregressive language model,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Gpt-j-6b: A 6 billion parameter autoregressive language model,

Reference 24

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Observation b83c480b-c9e5-416f-abf0-47e0637d48ff · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Low-bit Model Quantization for Deep Neural Networks: A Survey GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 25

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Observation c3ced501-4c15-4227-adbb-fb26f8be21f3 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Mistral 7B

Reference 26

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Observation 80ac65f1-8fdf-4fb1-b5c6-2b6008d72bf7 · outbound

This paper cites Starcoder: may the source be with you!.

Low-bit Model Quantization for Deep Neural Networks: A Survey Starcoder: may the source be with you!

Reference 27

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Observation a91eb1cf-6e9d-44f0-b042-b32e60b4baac · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Low-bit Model Quantization for Deep Neural Networks: A Survey Gemma: Open Models Based on Gemini Research and Technology

Reference 28

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Observation 1c272069-a21a-4d17-97ef-8304646565ff · outbound

This paper cites Training language models to follow instructions with human feedback,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Training language models to follow instructions with human feedback,

Reference 29

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Observation d31ea53e-08d8-4148-bf08-06079fe7f90d · outbound

This paper cites Zeroquant: Efficient and affordable post-training quantization for large-scale transformers,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Zeroquant: Efficient and affordable post-training quantization for large-scale transformers,

Reference 30

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Low-bit Model Quantization for Deep Neural Networks: A Survey Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 31

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Observation 7dbec1fb-aa5e-4346-88d9-ad669cbc4ed8 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Squeezellm: Dense-and-sparse quanti- zation,

Reference 32

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Observation b4314687-587d-484e-adf0-2c223c6b8d54 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Q-bert: Hessian based ultra low precision quantization of bert,

Reference 33

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Low-bit Model Quantization for Deep Neural Networks: A Survey Qlora: Efficient finetuning of quantized llms,

Reference 34

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Low-bit Model Quantization for Deep Neural Networks: A Survey Gptq: Accurate post-training quantization for generative pre-trained transformers,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Spqr: A sparse-quantized representation for near-lossless llm weight compression,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Awq: Activation-aware weight quantization for llm compression and acceleration,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Training with quantization noise for extreme model compression,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Obelics: An open web-scale filtered dataset of interleaved image-text documents,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Laion-5b: An open large-scale dataset for training next generation image-text models,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Wit: Wikipedia-based image text dataset for multimodal multilin- gual machine learning,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Gqa: A new dataset for real- world visual reasoning and compositional question answering,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Towards vqa models that can read,

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Observation a33d66cb-8069-4f73-ad12-c5bd4fa41f11 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Learn to explain: Multimodal reasoning via thought chains for science question answering,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Vizwiz grand challenge: Answering visual questions from blind people,

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Low-bit Model Quantization for Deep Neural Networks: A Survey Visual instruction tuning,

Reference 46

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Low-bit Model Quantization for Deep Neural Networks: A Survey Openflamingo: An open-source framework for training large autoregressive vision-language models,

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Observation 00f8d0aa-8305-4f75-b696-2a5e484d3cb1 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Noisyquant: Noisy bias-enhanced post-training activation quanti- zation for vision transformers,

Reference 48

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Observation 58aec02d-4de8-4902-aa20-68417328c341 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Imagenet large scale visual recognition challenge,

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

Low-bit Model Quantization for Deep Neural Networks: A Survey Learning multiple layers of features from tiny images,

Reference 50

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Observation 3986a7fd-ed68-4074-8fe4-e42c7337376b · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Microsoft COCO Captions: Data Collection and Evaluation Server

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Low-bit Model Quantization for Deep Neural Networks: A Survey LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 52

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Observation b46f1a0a-f61d-4d6b-8576-5654bbf11a35 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Mak- ing the v in vqa matter: Elevating the role of image understanding in visual question answering,

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Observation f8631432-f4db-43a2-af19-e3f25568717c · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Improved denoising diffusion probabilistic models,

Reference 54

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Observation 910598c7-bf96-4d8d-a1bd-d43e646b7538 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Denoising diffusion probabilistic models,

Reference 55

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Observation 42bb0e91-f8f9-46f6-b615-ad05928005c6 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Deep residual learning for image recognition,

Reference 56

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Observation e11a9ad8-cc00-43ec-9634-ba36bfa1dd14 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 57

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This paper cites A style-based generator archi- tecture for generative adversarial networks,.

Low-bit Model Quantization for Deep Neural Networks: A Survey A style-based generator archi- tecture for generative adversarial networks,

Reference 58

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Low-bit Model Quantization for Deep Neural Networks: A Survey Vila: On pre-training for visual language models,

Reference 59

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Low-bit Model Quantization for Deep Neural Networks: A Survey Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 60

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Observation 0e64118f-248d-4da1-ab8d-8ce10d91b2f3 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Qdrop: Randomly dropping quantization for extremely low-bit post-training quanti- zation,

Reference 61

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Low-bit Model Quantization for Deep Neural Networks: A Survey Post-training quantization on diffusion models,

Reference 62

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Observation d0493f47-99e2-4eaa-bd99-dcc2f06d67b9 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Q-DM: An efficient low-bit quantized diffusion model,

Reference 63

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Low-bit Model Quantization for Deep Neural Networks: A Survey Accurate post training quantization with small calibration sets,

Reference 64

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Observation d94c755a-7381-40a5-bd80-22e19f16b029 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Ntire 2017 challenge on single image super-resolution: Dataset and study,

Reference 65

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Observation b9c14db4-e7fe-4816-984b-13b71a5ab5dc · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Component divide-and-conquer for real-world image super- resolution,

Reference 66

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Observation ccf9d8d6-eef0-48d0-baa5-667f1af9ae2c · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Low-complexity single-image super-resolution based on nonnegative neighbor embedding,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:22.550019Z

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Observation 1619a4ba-1ee2-4f37-b610-dc2d798df6b4 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey On single image scale-up using sparse-representations,

Reference 68

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Observation c4423193-ebfc-4b23-ae3d-97a079b34c09 · outbound

This paper cites A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,.

Low-bit Model Quantization for Deep Neural Networks: A Survey A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,

Reference 69

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Observation 18b30f33-3014-4a81-bb81-72d82a44be7c · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Single image super- resolution from transformed self-exemplars,

Reference 70

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Observation e0e19478-2439-4718-b04d-a1ebdfc0fe8a · outbound

This paper cites Accurate image super-resolution using very deep convolutional networks,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Accurate image super-resolution using very deep convolutional networks,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:22.333916Z

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Low-bit Model Quantization for Deep Neural Networks: A Survey Enhanced deep residual networks for single image super-resolution,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:22.316244Z

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Low-bit Model Quantization for Deep Neural Networks: A Survey Residual dense network for image super-resolution,

Reference 73

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Observation 402371c4-fecf-4a7c-8fea-3983c8b2dabf · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Photo- realistic single image super-resolution using a generative adver- sarial network,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:22.087919Z

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Observation fe6c70d7-ff75-42d6-80a9-ae675da94a33 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Daq: Channel-wise distribution-aware quantization for deep image super-resolution networks,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:22.067706Z

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Observation 629789ed-85c1-4c6c-a977-a9d7c7879f90 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Searching for low-bit weights in quantized neural networks,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.885684Z

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Observation 0523555f-7e8d-4629-b374-b02e07ef61ad · outbound

This paper cites Learnable lookup table for neural network quantization,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Learnable lookup table for neural network quantization,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.791408Z

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Observation e31be9f7-2585-42b4-b7dd-b7f254dd9eda · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Encoder-decoder with atrous separable convolution for semantic image segmentation,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.773345Z

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Observation 913aba1e-6f9a-4615-8df7-43a5cc94e500 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Repvgg: Making vgg-style convnets great again,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.627957Z

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Observation 8b37d7d7-1dc6-400f-bf23-5566dca156a8 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Up or down? adaptive rounding for post-training quantization,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.563277Z

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Observation bd612af2-c7b9-4bca-a937-8d14eab618a5 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Designing network design spaces,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.545213Z

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Low-bit Model Quantization for Deep Neural Networks: A Survey Mnasnet: Platform-aware neural architecture search for mobile,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.526562Z

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Observation 3af671cc-f240-4d24-964b-c40529025a1a · outbound

This paper cites Very deep convolutional net- works for large-scale image recognition,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Very deep convolutional net- works for large-scale image recognition,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.471076Z

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Observation 1cfb2fda-c509-463c-bb82-ffc6deb89743 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Rethinking the inception architecture for computer vision,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.304035Z

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Observation 4b439f53-6ea3-42f0-a4c9-953faada2203 · outbound

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

Low-bit Model Quantization for Deep Neural Networks: A Survey Brecq: Pushing the limit of post-training quantization by block reconstruction,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.217667Z

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Observation 69755a2b-71e4-4b3d-925c-e796e3fd9c3d · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Hawq: Hessian aware quantization of neural networks with mixed-precision,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.199956Z

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Observation 5c0b2ae5-827d-4b3c-a3dd-8b1dd08c0655 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Overcoming oscillations in quantization-aware training,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.104647Z

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Observation 2f1c154f-a8ba-4759-96ad-0668e6a21009 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Focal loss for dense object detection,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.023172Z

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Observation c69edea0-5bfa-4fec-bba0-34479dc12a9b · outbound

This paper cites ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation.

Low-bit Model Quantization for Deep Neural Networks: A Survey ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation

Reference 89

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This paper cites A comprehen- sive survey on model quantization for deep neural networks in image classification,.

Low-bit Model Quantization for Deep Neural Networks: A Survey A comprehen- sive survey on model quantization for deep neural networks in image classification,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:21.003205Z

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Observation 038e160a-abbb-4a31-bb8d-9e6043671bf5 · outbound

This paper cites A survey of quantization methods for efficient neural network inference,.

Low-bit Model Quantization for Deep Neural Networks: A Survey A survey of quantization methods for efficient neural network inference,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:19.815967Z

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Observation 7a4e6d2a-9e2a-46d3-a366-7b37cf5ff968 · outbound

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Low-bit Model Quantization for Deep Neural Networks: A Survey Evaluating Quantized Large Language Models

Reference 92

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Observation 23c0c9f8-5cc3-43ae-a1ca-0f1bc939f365 · outbound

This paper cites Exploiting LLM Quantization.

Low-bit Model Quantization for Deep Neural Networks: A Survey Exploiting LLM Quantization

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T23:13:19.825641Z

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Observation 7b8721ce-bbf3-4a53-b4e0-5fcbc4afa193 · outbound

This paper cites Exploring post-training quantization in llms from comprehensive study to low rank compensation,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Exploring post-training quantization in llms from comprehensive study to low rank compensation,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.969696Z

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Observation 399b7d90-901d-4dbf-b466-da2133c0e6a7 · outbound

This paper cites Exploring quantization techniques for large-scale language models: Methods, challenges and future directions,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Exploring quantization techniques for large-scale language models: Methods, challenges and future directions,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.854574Z

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Observation 6c765d08-8b49-4e55-982c-f2b39e1461f2 · outbound

This paper cites The case for 4-bit precision: k-bit inference scaling laws,.

Low-bit Model Quantization for Deep Neural Networks: A Survey The case for 4-bit precision: k-bit inference scaling laws,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.741994Z

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Observation d15ba077-7584-4b14-86c8-05739f4400ce · outbound

This paper cites Only train once: A one-shot neural network training and pruning framework,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Only train once: A one-shot neural network training and pruning framework,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.723893Z

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Observation c1b7694b-31bc-4787-8757-b41a6da224e3 · outbound

This paper cites Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures,.

Low-bit Model Quantization for Deep Neural Networks: A Survey Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.706752Z

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Observation e3a67481-d2d0-46b9-b5c0-a751206324f3 · outbound

This paper cites Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning.

Low-bit Model Quantization for Deep Neural Networks: A Survey Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

Reference 99

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Low-bit Model Quantization for Deep Neural Networks: A Survey Accelerate cnn via recursive bayesian pruning,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:13:20.611473Z

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Pith citing papers

Observation a2ed310a-1707-4cc8-b7c9-254c3c8be47d · inbound

Zero-Shot Quantization via Weight-Space Arithmetic cites this paper.

Zero-Shot Quantization via Weight-Space Arithmetic Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 4

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Observation 29cfaa74-df41-4ec8-a78d-fe5a7e64354a · inbound

TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference cites this paper.

TinyNeRV: Compact Neural Video Representations via Capacity Scaling, Distillation, and Low-Precision Inference Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 60

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Observation d8d30dce-ae4f-479e-a965-2c7e9174fa02 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 179

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Observation 01dbdfb9-1caf-4e20-9e3d-ba1aa9cd5d94 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 179

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Observation 088da7db-f95a-4ad4-9896-94c0b86e197a · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 179

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Observation d241124c-6c77-482f-a11a-e77cc63a4737 · inbound

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization cites this paper.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.777018Z

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Observation da34c941-e438-4ce0-8660-5a33c323c6c7 · inbound

JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators cites this paper.

JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 34

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Observation 498a9f54-498e-45fa-9745-2b9a6764cc15 · inbound

JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators cites this paper.

JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 34

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Unavailable: canonical work link unavailable.