Paper Citation Record · LEDGER

A probabilistic framework for dynamic quantization

As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.10689.

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

pith.paper-citation-record.v1
2505.10689 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:12:21.792536Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

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verified fuzzy2
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External citation measurements

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Outbound references

Observation f1e3105e-aba0-4727-b163-1324bf527298 · outbound

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 1

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 2

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This paper cites Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference.

A probabilistic framework for dynamic quantization Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference

Reference 3

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Reference 4

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

A probabilistic framework for dynamic quantization Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 5

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This paper cites Token Merging: Your ViT But Faster.

A probabilistic framework for dynamic quantization Token Merging: Your ViT But Faster

Reference 6

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 7

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 8

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 9

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 12

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 13

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A probabilistic framework for dynamic quantization Unresolved cited work

Reference 14

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Observation 43f6972b-f6e3-40bc-894c-f7266b027fae · outbound

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

A probabilistic framework for dynamic quantization Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 18

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This paper cites LLM Post-Training: A Deep Dive into Reasoning Large Language Models.

A probabilistic framework for dynamic quantization LLM Post-Training: A Deep Dive into Reasoning Large Language Models

Reference 19

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This paper cites CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs.

A probabilistic framework for dynamic quantization CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs

Reference 20

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A probabilistic framework for dynamic quantization Unresolved cited work

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A probabilistic framework for dynamic quantization Unresolved cited work

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A probabilistic framework for dynamic quantization Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C

Reference 24

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A probabilistic framework for dynamic quantization Unresolved cited work

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A probabilistic framework for dynamic quantization A White Paper on Neural Network Quantization

Reference 30

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A probabilistic framework for dynamic quantization Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer

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A probabilistic framework for dynamic quantization Llama 2: Open Foundation and Fine-Tuned Chat Models

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This paper cites 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 10674–10685.

A probabilistic framework for dynamic quantization 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 10674–10685

Reference 40

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A probabilistic framework for dynamic quantization Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N

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A probabilistic framework for dynamic quantization Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 45

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A probabilistic framework for dynamic quantization In Proceedings of the IEEE/CVF international conference on computer vision

Reference 2019

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A probabilistic framework for dynamic quantization In International conference on machine learning

Reference 2021

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A probabilistic framework for dynamic quantization Proceedings of the National Academy of Sciences 121, 27 (2024), e2311878121

Reference 2024

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Pith citing papers

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