Paper Citation Record · LEDGER

Learning Interpretable Differentiable Logic Networks for Tabular Regression

As of 18 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 2 inbound Pith citation observations for arXiv:2505.23615.

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

pith.paper-citation-record.v1
2505.23615 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 54 of 54 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:56:14.484702Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-17T21:40:17.655393Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation 88e3bf4a-33fb-4cec-a041-30298381852b · outbound

This paper cites Deep differentiable logic gate networks,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Deep differentiable logic gate networks,

Reference 1

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Observation a4455620-cc70-4103-be19-265596db9e9b · outbound

This paper cites Convolutional differentiable logic gate networks,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Convolutional differentiable logic gate networks,

Reference 2

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Observation 6d6f52ad-f1df-4265-8068-bf9f68796031 · outbound

This paper cites Learning interpretable differentiable logic networks,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Learning interpretable differentiable logic networks,

Reference 3

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This paper cites Fuzzy sets as a basis for a theory of possibility,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Fuzzy sets as a basis for a theory of possibility,

Reference 4

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This paper cites Statistical metrics,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Statistical metrics,

Reference 5

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This paper cites Goertzel, M.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Goertzel, M

Reference 6

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Observation fd6c97ec-66f5-448c-9778-8d5617936883 · outbound

This paper cites Distilling a neural network into a soft decision tree,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Distilling a neural network into a soft decision tree,

Reference 7

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Observation 2f8baea9-1e72-40a3-b6dc-01b90b9ec46a · outbound

This paper cites Deep neural decision trees,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Deep neural decision trees,

Reference 8

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This paper cites Deep neural decision forests,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Deep neural decision forests,

Reference 9

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This paper cites Adaptive neural trees,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Adaptive neural trees,

Reference 10

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Observation 6d988305-c86d-4be7-8070-b5e003907141 · outbound

This paper cites Neural oblivious decision ensembles for deep learning on tabular data,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Neural oblivious decision ensembles for deep learning on tabular data,

Reference 11

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Observation f4c8d8b9-fdff-41f7-95d9-52091d72bb76 · outbound

This paper cites KAN: Kolmogorov-Arnold networks,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression KAN: Kolmogorov-Arnold networks,

Reference 12

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This paper cites Physics-informed neural networks: A deep learn- ing framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Physics-informed neural networks: A deep learn- ing framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 13

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Observation a54b3f1d-1eee-42c7-a129-cd9cd667ad7e · outbound

This paper cites DiffTaichi: Differentiable programming for physical simulation,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression DiffTaichi: Differentiable programming for physical simulation,

Reference 14

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Observation 6cdcacc9-9a1f-4ff9-ad5b-5128f86cf210 · outbound

This paper cites Discovering symbolic models from deep learning with inductive biases,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Discovering symbolic models from deep learning with inductive biases,

Reference 15

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Observation 0948c294-3f36-4252-b6c4-82aa868c69d8 · outbound

This paper cites Automatic differentiation in machine learning: A survey,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Automatic differentiation in machine learning: A survey,

Reference 16

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This paper cites Learning with differentiable algorithms,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Learning with differentiable algorithms,

Reference 17

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Observation aacae4fe-a795-4461-ba6e-2378762995e8 · outbound

This paper cites Soft-DTW: A differentiable loss function for time-series,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Soft-DTW: A differentiable loss function for time-series,

Reference 18

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This paper cites ”Why should I trust you?.

Learning Interpretable Differentiable Logic Networks for Tabular Regression ”Why should I trust you?

Reference 19

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This paper cites A unified approach to interpreting model predictions,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression A unified approach to interpreting model predictions,

Reference 20

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Observation b3bdc8b4-f45d-4a67-91a3-31bfff5ab641 · outbound

This paper cites Deep inside convolutional networks: Visualising image classification models and saliency maps,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Deep inside convolutional networks: Visualising image classification models and saliency maps,

Reference 21

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This paper cites Feature visualization,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Feature visualization,

Reference 22

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Observation 48e510b1-e89c-47de-a4b2-e1cecb460b7b · outbound

This paper cites Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation,

Reference 23

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Observation 13829d3d-cca0-47be-afbf-dfad0e42fd0a · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,

Reference 24

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This paper cites Ridge regression: Biased estimation for nonorthogonal prob- lems,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Ridge regression: Biased estimation for nonorthogonal prob- lems,

Reference 25

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This paper cites Regression shrinkage and selection via the Lasso,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Regression shrinkage and selection via the Lasso,

Reference 26

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This paper cites Generalized additive models,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Generalized additive models,

Reference 27

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This paper cites Predictive learning via rule ensembles,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Predictive learning via rule ensembles,

Reference 28

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This paper cites Generalized and scalable optimal sparse decision trees,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Generalized and scalable optimal sparse decision trees,

Reference 29

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This paper cites Supersparse linear integer models for optimized medical scoring sys- tems,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Supersparse linear integer models for optimized medical scoring sys- tems,

Reference 30

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This paper cites Logical Neural Networks.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Logical Neural Networks

Reference 31

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This paper cites Scalable rule-based representation learning for inter- pretable classification,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Scalable rule-based representation learning for inter- pretable classification,

Reference 32

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This paper cites Neural logic machines,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Neural logic machines,

Reference 33

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This paper cites Neural Logic Networks.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Neural Logic Networks

Reference 34

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Learning both weights and connections for efficient neural network,

Reference 35

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural net- works,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression The lottery ticket hypothesis: Finding sparse, trainable neural net- works,

Reference 36

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This paper cites Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,

Reference 37

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This paper cites XNOR-Net: Imagenet classification using binary convolutional neural networks,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression XNOR-Net: Imagenet classification using binary convolutional neural networks,

Reference 38

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This paper cites GPTQ: Accurate post-training quantiza- tion for generative pre-trained transformers,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression GPTQ: Accurate post-training quantiza- tion for generative pre-trained transformers,

Reference 39

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Atom: Low-bit quantization for efficient and accurate LLM serving,

Reference 40

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Distilling the knowledge in a neural network,

Reference 41

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This paper cites MiniLLM: Knowledge distillation of large language models,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression MiniLLM: Knowledge distillation of large language models,

Reference 42

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This paper cites FINN: A framework for fast, scalable binarized neural network inference,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression FINN: A framework for fast, scalable binarized neural network inference,

Reference 43

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

Learning Interpretable Differentiable Logic Networks for Tabular Regression Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 44

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Wide & deep learning for recommender systems,

Reference 45

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This paper cites SymPy: Symbolic computing in Python,.

Learning Interpretable Differentiable Logic Networks for Tabular Regression SymPy: Symbolic computing in Python,

Reference 46

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Learning Interpretable Differentiable Logic Networks for Tabular Regression The UCI machine learning repository,

Reference 47

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Unresolved cited work

Reference 48

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Optuna: A next-generation hyperpa- rameter optimization framework,

Reference 49

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Scikit-learn: Machine learning in Python,

Reference 50

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Pytorch: An imperative style, high-performance deep learn- ing library,

Reference 51

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Learning Interpretable Differentiable Logic Networks for Tabular Regression Ray: A distributed framework for emerging AI applications,

Reference 52

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Pith citing papers

Observation bb940fd7-0dd4-4748-9f56-87251ef140d2 · inbound

Learning Interpretable Differentiable Logic Networks for Time-Series Classification cites this paper.

Learning Interpretable Differentiable Logic Networks for Time-Series Classification Learning Interpretable Differentiable Logic Networks for Tabular Regression

Reference 4

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LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment cites this paper.

LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment Learning Interpretable Differentiable Logic Networks for Tabular Regression

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