Paper Citation Record · LEDGER

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2505.02537.

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

pith.paper-citation-record.v1
2505.02537 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:05:55.212825Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T17:51:29.832500Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T17:53:05.007180Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6b330d33-2b53-45a6-9379-a8d456f16f1e · outbound

This paper cites write newline.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations write newline

Reference 1

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Observation 9378f69d-bc9f-4c86-a5ad-26106dbe31a9 · outbound

This paper cites Machine bias.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Machine bias

Reference 2

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Observation 814010ea-4ffe-4287-a432-a6ec9a13c858 · outbound

This paper cites Continuously Differentiable Exponential Linear Units.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Continuously Differentiable Exponential Linear Units

Reference 3

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This paper cites Feedback prediction for blogs.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Feedback prediction for blogs

Reference 4

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Observation 21075a42-a349-4f62-96e2-dfb62390c400 · outbound

This paper cites and Guestrin, C.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Guestrin, C

Reference 5

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This paper cites and Silva, R.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Silva, R

Reference 6

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Observation 1df3f5a4-ed81-4476-a772-9ca1d6e3eb3f · outbound

This paper cites Fast and accurate deep network learning by exponential linear units (elus).

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Fast and accurate deep network learning by exponential linear units (elus)

Reference 7

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This paper cites and Velikova, M.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Velikova, M

Reference 8

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This paper cites and Farid, H.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Farid, H

Reference 9

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This paper cites R., Singh, S.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations R., Singh, S

Reference 10

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Observation 6f69c032-ad49-4a50-aa06-e566f2e58382 · outbound

This paper cites Incorporating second-order functional knowledge for better option pricing.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Incorporating second-order functional knowledge for better option pricing

Reference 11

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Observation 87723832-2836-44c0-bd91-80c6237cc4c7 · outbound

This paper cites Incorporating functional knowledge in neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Incorporating functional knowledge in neural networks

Reference 12

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Unresolved cited work

Reference 13

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Observation aa3fa419-2ca1-43c8-8a3a-4d7b930732f2 · outbound

This paper cites and Bengio, Y.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Bengio, Y

Reference 14

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This paper cites Maxout networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Maxout networks

Reference 15

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Observation ea042d7e-8fbf-4859-8679-770541a5f918 · outbound

This paper cites How to incorporate monotonicity in deep networks while preserving flexibility? in NeurIPS, 2019.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations How to incorporate monotonicity in deep networks while preserving flexibility? in NeurIPS, 2019

Reference 16

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Observation dfeca548-3405-4ba7-8c2b-1bb740f05258 · outbound

This paper cites Monotonic calibrated interpolated look-up tables.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Monotonic calibrated interpolated look-up tables

Reference 17

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Observation 62fe5640-397a-4c20-813f-afa30258ec27 · outbound

This paper cites Deep residual learning for image recognition.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Deep residual learning for image recognition

Reference 18

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Observation 7f7f306f-e94f-42bc-be36-9ac83151ef02 · outbound

This paper cites Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Reference 19

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This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 20

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Observation 69c28d6a-9aac-49ae-b65e-794ce70a36ad · outbound

This paper cites H., Tom, B., and Barrett, J.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations H., Tom, B., and Barrett, J

Reference 21

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Observation 5c3dad7a-06e4-4f5f-898f-781e12221c52 · outbound

This paper cites Deep learning with s-shaped rectified linear activation units.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Deep learning with s-shaped rectified linear activation units

Reference 22

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This paper cites and Lee, J.-S.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Lee, J.-S

Reference 23

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Observation f052d654-791b-4ef3-833f-c75c7d07b33c · outbound

This paper cites Self-normalizing neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Self-normalizing neural networks

Reference 24

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Observation 4b88608f-e9af-4ad5-9971-084f0a0ebecf · outbound

This paper cites Certified monotonic neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Certified monotonic neural networks

Reference 25

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Observation f7e9337b-5146-49cc-8795-1c1025c1d64d · outbound

This paper cites and Reichman, D.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Reichman, D

Reference 26

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Observation 42938ee5-8555-4ca2-82d8-a2818aa17bff · outbound

This paper cites Fast and flexible monotonic functions with ensembles of lattices.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Fast and flexible monotonic functions with ensembles of lattices

Reference 27

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Unresolved cited work

Reference 28

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Hinton, G

Reference 29

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Mart \' nez, M

Reference 30

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This paper cites Expressive monotonic neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Expressive monotonic neural networks

Reference 31

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Fully neural network based model for general temporal point processes

Reference 32

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Pytorch: An imperative style, high-performance deep learning library

Reference 33

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations On the expressive power of deep neural networks

Reference 34

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Sriraman, H

Reference 35

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Shankaranarayana, S

Reference 36

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This paper cites Simplified models of remaining useful life based on stochastic orderings.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Simplified models of remaining useful life based on stochastic orderings

Reference 37

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This paper cites and Abu-Mostafa, Y.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Abu-Mostafa, Y

Reference 38

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This paper cites Counterexample-guided learning of monotonic neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Counterexample-guided learning of monotonic neural networks

Reference 39

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This paper cites Review and comparison of commonly used activation functions for deep neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Review and comparison of commonly used activation functions for deep neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:55.324969Z

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Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Lopez-Paz, D

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:55.313695Z

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This paper cites Attention is all you need.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Attention is all you need

Reference 42

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This paper cites The resurgence of structure in deep neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations The resurgence of structure in deep neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:55.293855Z

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This paper cites and Louppe, G.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations and Louppe, G

Reference 44

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This paper cites Hierarchical lattice layer for partially monotone neural networks.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Hierarchical lattice layer for partially monotone neural networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:55.275522Z

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This paper cites Deep lattice networks and partial monotonic functions.

Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations Deep lattice networks and partial monotonic functions

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:55.264253Z

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Pith citing papers

Observation 7b74ae1d-0a08-4eba-b112-3c1690a168d8 · inbound

Functional Similarity Metric for Neural Networks: Overcoming Parametric Ambiguity via Activation Region Analysis cites this paper.

Reference 27

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