Paper Citation Record · LEDGER

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

As of 19 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:1908.05033.

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

pith.paper-citation-record.v1
1908.05033 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:30:12.687950Z

measured 47 of 47 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T12:35:58.613973Z

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

46 of 46 outbound references displayed

verified exact1
verified fuzzy20
unresolved24
parse uncertain0
malformed identifier0
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External citation measurements

60
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 233fe5ec-f69e-402b-bf96-016c018ebffc · outbound

This paper cites Mur- ray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete War- den, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Mur- ray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete War- den, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng

Reference 1

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Observation 1e5789c1-268d-47a7-b6b0-ee70ff0e33f8 · outbound

This paper cites Post-training 4-bit quantization of convolution networks for rapid-deployment.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Post-training 4-bit quantization of convolution networks for rapid-deployment

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.478969Z

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Observation 1f7e976e-2c93-4d09-89ab-c5ea8c4a5c56 · outbound

This paper cites UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks

Reference 3

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Observation 4a47291e-45e4-41cf-9546-451700cefe9f · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

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Observation e2b6effe-8971-4fd5-a473-f725e4e42e8a · outbound

This paper cites Deep learning with low precision by half-wave gaussian quantization.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Deep learning with low precision by half-wave gaussian quantization

Reference 5

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Source-reported events for the cited work

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Observation d70de664-69df-4470-a2a1-68cfdd9f9bca · outbound

This paper cites Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN).

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)

Reference 6

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Source-reported events for the cited work

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Observation 8ddb7ae0-79f5-464f-bc11-f7067c80aa78 · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.504778Z

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Observation f6826612-4119-400b-930c-5a23c086b400 · outbound

This paper cites BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Reference 8

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Observation e1f9170a-0526-43b7-b753-83574ec08b46 · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 9

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Observation b4d54868-9c44-4749-851e-5973cf01b9a7 · outbound

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Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Unresolved cited work

Reference 10

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Observation cac450b8-09b2-44db-846e-b33904d2a341 · outbound

This paper cites Highly efﬁcient 8-bit low precision in- ference of convolutional neural networks with intelcaffe.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Highly efﬁcient 8-bit low precision in- ference of convolutional neural networks with intelcaffe

Reference 11

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Observation b8756b3c-7600-4779-a7b3-1a9e6c95524e · outbound

This paper cites Horowitz, and William J.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Horowitz, and William J

Reference 12

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Observation b285d988-f858-4b10-9364-b7cdb0827350 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 13

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Observation ed9af858-9d77-4162-b444-beffa9f6e258 · outbound

This paper cites Deep residual learning for image recognition.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Deep residual learning for image recognition

Reference 14

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Observation 84f0e748-b882-43b3-93ce-623e2e499c18 · outbound

This paper cites Identity mappings in deep residual networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Identity mappings in deep residual networks

Reference 15

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Observation 309787fd-c206-4814-abc5-bf29b9f28245 · outbound

This paper cites Binarized neural networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Binarized neural networks

Reference 16

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Source-reported events for the cited work

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Observation eb280faf-7835-4e7e-b1b6-809fd1e01ae3 · outbound

This paper cites gemmlowp: a small self-contained low- precision gemm library.(2017), 2017.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks gemmlowp: a small self-contained low- precision gemm library.(2017), 2017

Reference 17

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Observation ac85a8cf-d746-477b-a39b-ddb640779061 · outbound

This paper cites Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.272318Z

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Observation 8f5229a0-a0c4-499e-877b-0549371c3505 · outbound

This paper cites Caffe: Convolutional Architecture for Fast Feature Embedding.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Caffe: Convolutional Architecture for Fast Feature Embedding

Reference 19

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Observation 19b40e55-6add-4913-82a2-dd569eed5c5d · outbound

This paper cites Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Reference 20

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Observation 37bfd7f3-7d98-4d1f-b1f3-a26610ac5b4f · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.571293Z

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Observation beec229a-7903-4d02-8ebe-6bb8354e6690 · outbound

This paper cites The cifar-10 dataset.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks The cifar-10 dataset

Reference 22

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Observation 002faf3f-d17e-4f33-b605-dabdee647bb4 · outbound

This paper cites Ternary Weight Networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Ternary Weight Networks

Reference 23

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Observation b768bc15-0299-4da2-8d6f-46b9b74b4028 · outbound

This paper cites Training Quantized Nets: A Deeper Understanding.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Training Quantized Nets: A Deeper Understanding

Reference 24

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Observation abf7d159-c959-4b67-8a54-55fcca422f41 · outbound

This paper cites Towards accu- rate binary convolutional neural network.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Towards accu- rate binary convolutional neural network

Reference 25

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Observation 6209fbc2-b13e-49b6-a0f3-1bdab8d2b3a1 · outbound

This paper cites Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Reference 26

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Observation 2b336f12-bec0-4306-9db4-c2c5c2ebc51b · outbound

This paper cites 8-bit inference with tensorrt.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks 8-bit inference with tensorrt

Reference 27

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Observation 94bfe06c-e5aa-471c-87b5-66fef4e0ceca · outbound

This paper cites Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy

Reference 28

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Observation a3130958-cf88-4ee2-8924-7b9df55a24ba · outbound

This paper cites WRPN: Wide Reduced-Precision Networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks WRPN: Wide Reduced-Precision Networks

Reference 29

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Observation ca303bc8-0687-486a-8768-de164885e533 · outbound

This paper cites Convolutional Neural Networks using Logarithmic Data Representation.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Convolutional Neural Networks using Logarithmic Data Representation

Reference 30

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Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Unresolved cited work

Reference 31

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Observation 4614ed80-13d0-47e6-90e7-17061d0178c2 · outbound

This paper cites Automatic dif- ferentiation in PyTorch.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Automatic dif- ferentiation in PyTorch

Reference 32

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Observation a8eff7ea-18ad-49d5-be4e-43c658bd7dff · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Xnor-net: Imagenet classiﬁcation using bi- nary convolutional neural networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.185951Z

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Observation 71809c50-c279-49b5-bf8a-f76725481a4c · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.170314Z

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Observation bdaafabd-5e45-4139-9dbe-accc392c4b5c · outbound

This paper cites Clip-q: Deep network com- pression learning by in-parallel pruning-quantization.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Clip-q: Deep network com- pression learning by in-parallel pruning-quantization

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.155512Z

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Observation b39c5242-8a55-4f1a-a30c-c5bb0142bac9 · outbound

This paper cites HAQ: Hardware-Aware Automated Quantization with Mixed Precision.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks HAQ: Hardware-Aware Automated Quantization with Mixed Precision

Reference 36

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Observation fb7f629c-054e-4038-bdb8-5e89a4c1c047 · outbound

This paper cites Two-step quantization for low-bit neural networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Two-step quantization for low-bit neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.140309Z

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Observation 32c5b0db-d171-40c0-8f5a-53b3cd8b1932 · outbound

This paper cites Deep neural network compression with sin- gle and multiple level quantization.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Deep neural network compression with sin- gle and multiple level quantization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.123388Z

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Observation a3f237a0-eb88-4255-929b-58f2234d3773 · outbound

This paper cites ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks

Reference 39

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Source-reported events for the cited work

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Observation ba660f94-502c-4b7c-93cd-17aff01985e8 · outbound

This paper cites Blended coarse gradient descent for full quantization of deep neural networks.Research in the Mathematical Sciences, 6(1):14, 2019.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Blended coarse gradient descent for full quantization of deep neural networks.Research in the Mathematical Sciences, 6(1):14, 2019

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.108480Z

Source-reported events for the cited work

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Observation dbf481f0-27dc-4b7c-ba81-489593f88557 · outbound

This paper cites Lq-nets: Learned quantization for highly accurate and compact deep neural networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Lq-nets: Learned quantization for highly accurate and compact deep neural networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.093000Z

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Observation be603676-fba5-4335-bf04-16d5ee649d12 · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.669079Z

Source-reported events for the cited work

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Observation b57b5b80-a6a2-4626-87a6-134698ae714f · outbound

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

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.673773Z

Source-reported events for the cited work

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Observation 108aa247-b971-44f2-a468-21932eecd631 · outbound

This paper cites Balanced quantization: An effective and ef- ﬁcient approach to quantized neural networks.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Balanced quantization: An effective and ef- ﬁcient approach to quantized neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.078182Z

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Observation 03f818ed-6148-41dc-92d1-5c49522004f7 · outbound

This paper cites Trained Ternary Quantization.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Trained Ternary Quantization

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-14T13:30:12.683166Z

Source-reported events for the cited work

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Observation b6e43a48-5df1-4bb7-ac94-4fe1edfe1a59 · outbound

This paper cites Adap- tive layerwise quantization for deep neural network compres- sion.

Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks Adap- tive layerwise quantization for deep neural network compres- sion

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:30:13.062744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 38b8c48b-8b45-4c8c-a722-0190c285d970 · inbound

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space cites this paper.

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:36:55.878106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.