Paper Citation Record · LEDGER

Progressive Element-wise Gradient Estimation for Neural Network Quantization

As of 8 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.00097.

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

pith.paper-citation-record.v1
2509.00097 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:16:58.542609Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact1
verified fuzzy6
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2b9b8a6c-7bcd-4fc4-abf3-df34b5db28f7 · outbound

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

Progressive Element-wise Gradient Estimation for Neural Network Quantization Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.375783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b0db20e-7738-43b6-8ff7-0811f4210e19 · outbound

This paper cites PACT: Parameterized Clipping Activation for Quantized Neural Networks.

Progressive Element-wise Gradient Estimation for Neural Network Quantization PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.385499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7a3e75d-4f67-4156-b75b-15e8b0c57d0e · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.394395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41d0a812-6b18-4e6d-aa92-aa55a80c09a6 · outbound

This paper cites Learned Step Size Quantization.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Learned Step Size Quantization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.407678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c542e0d8-f167-4117-8c92-dad740a93e9c · outbound

This paper cites Differ- entiable soft quantization: Bridging full-precision and l ow- bit neural networks.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Differ- entiable soft quantization: Bridging full-precision and l ow- bit neural networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.321369Z

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.

Observation 26a1064b-db22-4030-af91-33115b5c2b46 · outbound

This paper cites Learning to quantize deep networks by op- timizing quantization intervals with task loss.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Learning to quantize deep networks by op- timizing quantization intervals with task loss

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.286966Z

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.

Observation b6b3bcc0-3de0-4cf0-b91e-82f0137a3351 · outbound

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

Progressive Element-wise Gradient Estimation for Neural Network Quantization Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.439477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41807cc8-c832-42fe-bd59-ac673abdeb87 · outbound

This paper cites Network quantization with element-wise gradient scaling.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Network quantization with element-wise gradient scaling

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.250606Z

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.

Observation 502681ad-5ac3-4bc8-b17d-82b1fb507a91 · outbound

This paper cites Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.477426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d29ec5-b6d0-44ca-a8ad-c22d5bf0922c · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Progressive Element-wise Gradient Estimation for Neural Network Quantization BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.488499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eafdccf2-2892-40e1-a880-2b1f36cb1ebf · outbound

This paper cites MQBench: Towards Reproducible and Deployable Model Quantization Benchmark.

Progressive Element-wise Gradient Estimation for Neural Network Quantization MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:16:58.759186Z

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.

Observation ecefb5b8-54f1-41bb-b41c-5dc8a5b02f5b · outbound

This paper cites Xnor-net: Imagenet classiﬁcation using bi - nary convolutional neural networks.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Xnor-net: Imagenet classiﬁcation using bi - nary convolutional neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.222742Z

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.

Observation 890075e0-45f9-4d40-a484-93bfd958f4c9 · outbound

This paper cites Towards resource-efﬁcient edge ai: From federated learning to semi - supervised model personalization.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Towards resource-efﬁcient edge ai: From federated learning to semi - supervised model personalization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.187888Z

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.

Observation 37d70139-a974-4bcf-af49-2bad8a844f68 · outbound

This paper cites Automatic at- tention pruning: Improving and automating model pruning using attentions.

Progressive Element-wise Gradient Estimation for Neural Network Quantization Automatic at- tention pruning: Improving and automating model pruning using attentions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:16:59.135088Z

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.

Observation 72ca8798-adce-4e6a-b8bd-1a03b244732f · outbound

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

Progressive Element-wise Gradient Estimation for Neural Network Quantization DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T15:16:58.542609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.