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Paper Citation Record · LEDGER

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task

As of 12 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2412.03915.

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

pith.paper-citation-record.v1
2412.03915 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:00:08.291409Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

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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Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 375b1730-46e6-4cf7-bc7b-7132b641bffb · outbound

This paper cites an unresolved cited work.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 1

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This paper cites Tell Me Where to Look: Guided Attention Inference Network.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Tell Me Where to Look: Guided Attention Inference Network

Reference 2

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This paper cites Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation

Reference 3

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Sasan, A

Reference 4

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This paper cites R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D

Reference 5

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 6

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This paper cites Stochastic Gradient Descent as Approximate Bayesian Inference.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Stochastic Gradient Descent as Approximate Bayesian Inference

Reference 7

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Observation 9056f880-b3de-4b27-99af-03d8a833a86a · outbound

This paper cites Investigating the influence of noise and distractors on the interpretation of neural networks.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Investigating the influence of noise and distractors on the interpretation of neural networks

Reference 8

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task SmoothGrad: removing noise by adding noise

Reference 9

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 10

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This paper cites R., & Samek, W.

Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task R., & Samek, W

Reference 11

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 12

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 13

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Unresolved cited work

Reference 15

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Maknoon, R

Reference 16

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 17

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Conservative algebras of $2$-dimensional algebras, V

Reference 18

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Feizi, S

Reference 19

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Owl: A General-Purpose Numerical Library in OCaml

Reference 20

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Theiler, J

Reference 21

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Hinton, G

Reference 22

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Doshi-Velez, F

Reference 23

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task PACT: Parameterized Clipping Activation for Quantized Neural Networks

Reference 24

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Sasan, A

Reference 25

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Burges, C

Reference 26

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Hinton, G

Reference 27

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Novel Saliency Analysis for the Forward Forward Algorithm

Reference 28

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Captum: A unified and generic model interpretability library for PyTorch

Reference 29

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task & Sasan, A

Reference 30

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 31

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Pith citing papers

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