Paper Citation Record · LEDGER

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters

As of 13 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.16677.

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

pith.paper-citation-record.v1
2501.16677 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:32:52.154908Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 14d3fc98-f16f-4b4f-8eee-1af16f91246c · outbound

This paper cites an unresolved cited work.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Unresolved cited work

Reference 1

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This paper cites Constraint answer set programming without grounding.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Constraint answer set programming without grounding

Reference 2

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This paper cites Knowledge representation, reasoning and declarative problem solving.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Knowledge representation, reasoning and declarative problem solving

Reference 3

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This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 4

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Imagenet: A large-scale hierarchical image database

Reference 5

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This paper cites Extraction of Salient Sentences from Labelled Documents.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Extraction of Salient Sentences from Labelled Documents

Reference 6

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This paper cites Accelerating convolutional neural networks via activation map compression.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Accelerating convolutional neural networks via activation map compression

Reference 7

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Unresolved cited work

Reference 8

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This paper cites Deep learning using computer vision in self driving cars for lane and traffic sign detection.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Deep learning using computer vision in self driving cars for lane and traffic sign detection

Reference 9

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This paper cites Elite backprop: Training sparse interpretable neurons.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Elite backprop: Training sparse interpretable neurons

Reference 10

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This paper cites Sparseout: Controlling sparsity in deep networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Sparseout: Controlling sparsity in deep networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.687629Z

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This paper cites Kingma and Jimmy Ba.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Kingma and Jimmy Ba

Reference 12

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This paper cites Survey of computer vision-based natural disaster warning systems.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Survey of computer vision-based natural disaster warning systems

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.656599Z

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This paper cites Inducing and exploiting activation sparsity for fast inference on deep neural networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Inducing and exploiting activation sparsity for fast inference on deep neural networks

Reference 14

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Observation 44c22370-ba91-4a76-849c-7bb8996a1e4d · outbound

This paper cites Human evaluation of models built for interpretability.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Human evaluation of models built for interpretability

Reference 15

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This paper cites Boser, John S.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Boser, John S

Reference 16

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This paper cites Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond

Reference 17

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This paper cites Training interpretable convolutional neural networks by differentiating class-specific filters.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Training interpretable convolutional neural networks by differentiating class-specific filters

Reference 18

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This paper cites A survey of sparse-learning methods for deep neural networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters A survey of sparse-learning methods for deep neural networks

Reference 19

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Extracting meaningful high-fidelity knowledge from convolutional neural networks

Reference 20

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Observation 79290388-0f69-4d25-bb6c-c48e8e01fe4d · outbound

This paper cites Nesyfold: A framework for interpretable image classification.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Nesyfold: A framework for interpretable image classification

Reference 21

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This paper cites Using logic programming and kernel-grouping for improving interpretability of convolutional neural networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Using logic programming and kernel-grouping for improving interpretability of convolutional neural networks

Reference 22

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This paper cites A neurosymbolic framework for bias correction in convolutional neural networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters A neurosymbolic framework for bias correction in convolutional neural networks

Reference 23

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Toward transparent ai: A survey on interpreting the inner structures of deep neural networks

Reference 24

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.473405Z

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Observation eee9b09a-8340-4a8a-82f0-ac854e484c33 · outbound

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Interpretable Compositional Convolutional Neural Networks

Reference 26

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Dropout: a simple way to prevent neural networks from overfitting

Reference 28

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Stallkamp, M

Reference 29

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Sun, Bin Zheng, and Wei Qian

Reference 30

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Tickle, R

Reference 31

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Eric: Extracting relations inferred from convolutions

Reference 32

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This paper cites On the explainability of convolutional layers for multi-class problems.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters On the explainability of convolutional layers for multi-class problems

Reference 33

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This paper cites FOLD-SE: an efficient rule-based machine learning algorithm with scalable explainability.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters FOLD-SE: an efficient rule-based machine learning algorithm with scalable explainability

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.368442Z

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This paper cites Dasnet: Dynamic activation sparsity for neural network efficiency improvement.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Dasnet: Dynamic activation sparsity for neural network efficiency improvement

Reference 35

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Visualizing and understanding convolutional networks

Reference 36

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This paper cites Interpretable convolutional neural networks.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Interpretable convolutional neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.322091Z

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters A survey on neural network interpretability

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.306917Z

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Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Object detectors emerge in deep scene cnns

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.290984Z

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This paper cites Places: A 10 million image database for scene recognition, 2017.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters Places: A 10 million image database for scene recognition, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:32:52.275592Z

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Observation 7bedd5c4-64a9-428d-bf83-884a5abc8d83 · outbound

This paper cites write newline.

Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters write newline

Reference 41

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.