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Paper Citation Record · LEDGER

Input convex neural networks as surrogates in mathematical optimisation

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2608.09707.

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

pith.paper-citation-record.v1
2608.09707 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:21:10.258752Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

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

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 86804e03-fcc7-4361-8d49-58af699bd8db · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 1

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Observation 1c6bcdb1-6762-45c3-9a58-6cf7bd76dc35 · outbound

This paper cites Input Convex Neural Networks.

Input convex neural networks as surrogates in mathematical optimisation Input Convex Neural Networks

Reference 2

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 3

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 4

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 5

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 6

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Input convex neural networks as surrogates in mathematical optimisation Maximum Resilience of Artificial Neural Networks

Reference 7

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Observation d11a501a-0a22-4279-a763-f2a9a578ef23 · outbound

This paper cites A Survey of Model Compression and Acceleration for Deep Neural Networks.

Input convex neural networks as surrogates in mathematical optimisation A Survey of Model Compression and Acceleration for Deep Neural Networks

Reference 8

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 9

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Input convex neural networks as surrogates in mathematical optimisation B., & Bent, R

Reference 10

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 11

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 12

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 13

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 14

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 15

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation & Andersson, H

Reference 17

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Input convex neural networks as surrogates in mathematical optimisation Gurobi Optimizer Reference Manual

Reference 18

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation & Hall, J

Reference 20

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation S., Ali, M., & Pistikopoulos, E

Reference 24

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Input convex neural networks as surrogates in mathematical optimisation H., Kwon, H., & Jeong, H

Reference 25

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Input convex neural networks as surrogates in mathematical optimisation Adam: A Method for Stochastic Optimization

Reference 26

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation & Oliveira, F

Reference 28

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Input convex neural networks as surrogates in mathematical optimisation ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming

Reference 29

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 30

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Input convex neural networks as surrogates in mathematical optimisation Optimal transport mapping via input convex neural networks

Reference 31

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Input convex neural networks as surrogates in mathematical optimisation & Wiberg, H

Reference 32

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Input convex neural networks as surrogates in mathematical optimisation \.I ., den Hertog, D., & Fajemisin, A

Reference 33

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Input convex neural networks as surrogates in mathematical optimisation Bounding and Counting Linear Regions of Deep Neural Networks

Reference 36

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Input convex neural networks as surrogates in mathematical optimisation Evaluating Robustness of Neural Networks with Mixed Integer Programming

Reference 37

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T12:21:10.228462Z digest=sha256:5dbd09103501ca0d1f4e384733f6c7740cb6890b73984b76bd89a32a1562efe7

Observation 34145bd5-a75e-4da2-bef8-ce3260088617 · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T12:21:10.231989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:21:10.231989Z digest=sha256:735020da0b7e5ca97840697900b62393f429e63dc96e066f998cf45c45057aba

Observation c481a939-8b02-4072-a6a1-6f3857ca3b8e · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 42

Resolution
verified exact
raw_fallback, observed 2026-08-11T12:21:10.921660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T12:21:10.235607Z digest=sha256:1ffd40bbde62f8245776a85e48d50b642420ee38c60902e7a6b12de87d2c4fc3

Observation 6fc183ec-fc43-4d91-99c0-980574a7c248 · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T12:21:10.238915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:21:10.238915Z digest=sha256:56e3f185e2287b618b58010d1f530ad9bc55067286565b99b41a695e057ca034

Observation 0d90c5ea-404f-4d1e-a256-d96920c02744 · outbound

This paper cites & Wang, J.

Input convex neural networks as surrogates in mathematical optimisation & Wang, J

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T12:21:10.244557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:21:10.244557Z digest=sha256:783e5f369697daec978149f8edb3fcc72acab0ba605dd574d14ee232d0a58b1c

Observation a3bef834-9907-42e4-b36b-7081ff430798 · outbound

This paper cites & Bequette, B.

Input convex neural networks as surrogates in mathematical optimisation & Bequette, B

Reference 45

Resolution
metadata mismatch
raw_fallback, observed 2026-08-11T12:21:10.775690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T12:21:10.247999Z digest=sha256:7b2582219ec136b5c37c2217244fa6df56d64353dc869f581307d3e44f59edc9

Observation eda421ef-8617-4916-aea4-1d92399dc817 · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T12:21:10.251136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:21:10.251136Z digest=sha256:1da820104685303c8bd9678835e822014afd0cb9edd8289a0453d79032720c87

Observation e6eec090-0f21-4e1a-b376-93d94d13e513 · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 47

Resolution
metadata mismatch
raw_fallback, observed 2026-08-11T12:21:10.612497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T12:21:10.255354Z digest=sha256:c708d96c711fc1ee60d2ca730cf4b9792618d86362c6c25aa320e6c42c825e5f

Observation f857a294-5af3-48a8-98c3-00dc515c7dbe · outbound

This paper cites an unresolved cited work.

Input convex neural networks as surrogates in mathematical optimisation Unresolved cited work

Reference 48

Resolution
verified exact
doi, observed 2026-08-11T12:21:10.292818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-11T12:21:10.258752Z digest=sha256:7c48c8a6350b190b91ae49541366e622da8ab82318a6731c22c0f0ab25bfd464

Pith citing papers

No inbound Pith citation observations are available.