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Paper Citation Record · LEDGER

Precision Neural Network Quantization via Learnable Adaptive Modules

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2504.17263.

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

pith.paper-citation-record.v1
2504.17263 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:50:08.105652Z

measured 38 of 38 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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External citation measurements

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Outbound references

Observation 8d25a028-306a-4770-b771-a002a174e2f6 · outbound

This paper cites an unresolved cited work.

Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 1

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 2

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This paper cites Attention Is All You Need.

Precision Neural Network Quantization via Learnable Adaptive Modules Attention Is All You Need

Reference 3

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 4

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Observation c83c2816-6fea-4209-acd4-0b6beb88bd31 · outbound

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

Precision Neural Network Quantization via Learnable Adaptive Modules PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 5

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Observation 2e306230-213c-4a50-a579-048e0c537673 · outbound

This paper cites Learned Step Size Quantization.

Precision Neural Network Quantization via Learnable Adaptive Modules Learned Step Size Quantization

Reference 6

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This paper cites The State of Sparsity in Deep Neural Networks.

Precision Neural Network Quantization via Learnable Adaptive Modules The State of Sparsity in Deep Neural Networks

Reference 7

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This paper cites Blalock, J.

Precision Neural Network Quantization via Learnable Adaptive Modules Blalock, J

Reference 8

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 9

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 10

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 11

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This paper cites Elsken, J.

Precision Neural Network Quantization via Learnable Adaptive Modules Elsken, J

Reference 12

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This paper cites Howard, M.

Precision Neural Network Quantization via Learnable Adaptive Modules Howard, M

Reference 13

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 14

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This paper cites Gholami, S.

Precision Neural Network Quantization via Learnable Adaptive Modules Gholami, S

Reference 15

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Precision Neural Network Quantization via Learnable Adaptive Modules Bhalgat, J

Reference 16

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Observation 47219819-14f7-4d8e-aaf8-c992860eee62 · outbound

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

Precision Neural Network Quantization via Learnable Adaptive Modules Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 17

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 18

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

Precision Neural Network Quantization via Learnable Adaptive Modules Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

Reference 19

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 20

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Precision Neural Network Quantization via Learnable Adaptive Modules Zhang, J

Reference 21

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Precision Neural Network Quantization via Learnable Adaptive Modules Convolutional Neural Networks using Logarithmic Data Representation

Reference 22

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

Precision Neural Network Quantization via Learnable Adaptive Modules Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks

Reference 23

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Precision Neural Network Quantization via Learnable Adaptive Modules Unresolved cited work

Reference 24

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Precision Neural Network Quantization via Learnable Adaptive Modules DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 25

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Precision Neural Network Quantization via Learnable Adaptive Modules Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 27

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Precision Neural Network Quantization via Learnable Adaptive Modules Sandler, A

Reference 29

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Precision Neural Network Quantization via Learnable Adaptive Modules Krizhevsky, et al., Learning multiple layers of features from tiny images (2009)

Reference 31

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Precision Neural Network Quantization via Learnable Adaptive Modules SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 32

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Reference 38

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Pith citing papers

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