Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification | Paper Citation Record

Observation eb27ed11-024b-44b4-98af-766c11448e2e · outbound

This paper cites Strong Mixed-Integer Programming Formulations for Trained Neural Networks.Mathematical Programming, 183(1):3–39, 2020.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Strong Mixed-Integer Programming Formulations for Trained Neural Networks.Mathematical Programming, 183(1):3–39, 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.447393Z

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This paper cites an unresolved cited work.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:38:01.429771Z

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Observation 2b2b3b5d-00b1-42d2-b5be-8cae81bbd1fb · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Neural Machine Translation by Jointly Learning to Align and Translate

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:37:59.943361Z

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Observation 9f9cab6f-c874-4be6-aad9-648f52b4b491 · outbound

This paper cites Measuring Neural Net Robustness With Constraints.Advances in Neural Information Processing Systems, 29, 2016.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Measuring Neural Net Robustness With Constraints.Advances in Neural Information Processing Systems, 29, 2016

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.413029Z

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Observation 0c0d51ee-f3f2-420a-b96e-8655132d794d · outbound

This paper cites Efficient Neural Network Verification via Layer-Based Semidefinite Relaxations and Linear Cuts.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Efficient Neural Network Verification via Layer-Based Semidefinite Relaxations and Linear Cuts

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.395094Z

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Observation 4205f471-b8b4-47ee-86d3-b822546de9fd · outbound

This paper cites Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.373582Z

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Observation efdf2190-10a7-456b-ab2d-a10f81e865ca · outbound

This paper cites Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.Foundations and Trends® in Machine Learning, 3(1):1–122, 2011.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers.Foundations and Trends® in Machine Learning, 3(1):1–122, 2011

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.358349Z

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Observation a7caee1c-ab5a-4df4-89da-b7922f5f0070 · outbound

This paper cites The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:37:59.970837Z

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Observation 8922dc2c-e6ba-42cf-90c5-d11a82a823bb · outbound

This paper cites A Unified View of Piecewise Linear Neural Network Verification.Advances in Neural Information Processing Systems, 31, 2018.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A Unified View of Piecewise Linear Neural Network Verification.Advances in Neural Information Processing Systems, 31, 2018

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.343035Z

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Observation 23ce62de-79cf-4423-be84-30f1f2ef87c5 · outbound

This paper cites End-to-End Autonomous Driving: Challenges and Frontiers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification End-to-End Autonomous Driving: Challenges and Frontiers.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.321548Z

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Observation f24c578c-c827-411b-a41f-8a7f29d65758 · outbound

This paper cites Maximum Resilience of Artificial Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Maximum Resilience of Artificial Neural Networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.302720Z

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Observation 359f69a0-ab43-4caa-858c-ae5dcfc4ac9b · outbound

This paper cites Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.284056Z

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Observation d31dd728-f8b3-4d0f-9bee-b8e1a48a32fc · outbound

This paper cites Certified Adversarial Robustness via Randomized Smoothing.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Certified Adversarial Robustness via Randomized Smoothing

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.266672Z

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Observation 742f1e58-fa4e-4153-8014-2d17e8b1a5aa · outbound

This paper cites Enabling Certification of Verification-Agnostic Networks via Memory-Efficient Semidefinite Programming.Advances in Neural Information Processing Systems, 33:5318–5331, 2020.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Enabling Certification of Verification-Agnostic Networks via Memory-Efficient Semidefinite Programming.Advances in Neural Information Processing Systems, 33:5318–5331, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.243796Z

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Observation a971e7f6-f969-47bf-9147-d45bc19c10a4 · outbound

This paper cites The MNIST Database of Handwritten Digit Images for Machine Learning Research.IEEE Signal Processing Magazine, 29(6):141–142, 2012.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification The MNIST Database of Handwritten Digit Images for Machine Learning Research.IEEE Signal Processing Magazine, 29(6):141–142, 2012

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.221657Z

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Observation 6c7b0dd2-4b9d-4935-824c-7fc0e0e23e90 · outbound

This paper cites Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.193667Z

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Observation c6e8a0f4-c70b-40bd-91e2-c461c998438c · outbound

This paper cites Scalable Approximate Optimal Diagonal Preconditioning.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Scalable Approximate Optimal Diagonal Preconditioning

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:38:00.452826Z

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Observation b6e553df-87ac-4f75-9fd1-46bad7cac339 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Explaining and Harnessing Adversarial Examples

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:00.048224Z

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Observation 7014157b-eece-44f6-83bc-fb1efbe6a534 · outbound

This paper cites Efficient Neural Network Verification via Adaptive Refinement and Adversarial Search.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Efficient Neural Network Verification via Adaptive Refinement and Adversarial Search

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.172088Z

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Observation d210fdf7-ac1c-465f-885e-ce98a381b7c2 · outbound

This paper cites Cambridge University Press, Cambridge, England, 2 edition, Oct 2012.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Cambridge University Press, Cambridge, England, 2 edition, Oct 2012

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.144423Z

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Observation c174aaf8-5b54-4e0f-91f2-4dd45ba878f6 · outbound

This paper cites Facial Reduction for Symmetry Reduced Semidefinite and Doubly Nonnegative Programs.Mathematical Programming, 200(1):475–529, 2023.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Facial Reduction for Symmetry Reduced Semidefinite and Doubly Nonnegative Programs.Mathematical Programming, 200(1):475–529, 2023

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.121389Z

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Observation c36626f0-75e3-4888-9860-9719097ef45c · outbound

This paper cites Safety Verification of Deep Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Safety Verification of Deep Neural Networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.089530Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation caa7b2e8-4212-45cc-ae7e-99b185c070c8 · outbound

This paper cites Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.065828Z

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Observation 93212bfb-4f0a-4a6f-b848-ba7fc20a5d54 · outbound

This paper cites Reluplex: A Calculus for Reasoning About Deep Neural Networks.Formal Methods in System Design, 60(1):87–116, 2022.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Reluplex: A Calculus for Reasoning About Deep Neural Networks.Formal Methods in System Design, 60(1):87–116, 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.046938Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2df4720a-bf95-4ded-99bf-546790fea349 · outbound

This paper cites ImageNet Classification With Deep Convolutional Neural Networks.Advances in Neural Information Processing Systems, 25, 2012.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification ImageNet Classification With Deep Convolutional Neural Networks.Advances in Neural Information Processing Systems, 25, 2012

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.027303Z

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Observation 65bb556a-69e2-4117-8c8c-c2d26fb767e0 · outbound

This paper cites A Semidefinite Relaxation Based Branch-and-Bound Method for Tight Neural Network Verification.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A Semidefinite Relaxation Based Branch-and-Bound Method for Tight Neural Network Verification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:01.004526Z

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Observation ebf4718c-cf66-4537-a47b-47604d9e1cc7 · outbound

This paper cites World Scientific, 2009.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification World Scientific, 2009

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.980311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b01fb00d-d5d1-49ca-97a0-258b87244836 · outbound

This paper cites SoK: Certified Robustness for Deep Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification SoK: Certified Robustness for Deep Neural Networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.959173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 86d3e5c2-fcd1-4c3c-b8bc-acce2f3afcd2 · outbound

This paper cites An ADMM-Based Interior-Point Method for Large-Scale Linear Programming.Optimization Methods and Software, 36(2-3):389–424, 2021.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification An ADMM-Based Interior-Point Method for Large-Scale Linear Programming.Optimization Methods and Software, 36(2-3):389–424, 2021

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.942608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cbfcaa1f-abaa-4462-a3a9-60d6d0990fb8 · outbound

This paper cites An approach to reachability analysis for feed-forward ReLU neural networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification An approach to reachability analysis for feed-forward ReLU neural networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:00.143913Z

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Observation e9fa1202-0b32-4441-8240-78730385353c · outbound

This paper cites A Structural Geometrical Analysis of Weakly Infeasible SDPs.Journal of the Operations Research Society of Japan, 59(3):241–257, 2016.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A Structural Geometrical Analysis of Weakly Infeasible SDPs.Journal of the Operations Research Society of Japan, 59(3):241–257, 2016

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.920270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90aaa070-9ef6-42d4-84dd-4df349a5eff2 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.International Conference on Learning Representations, 2018.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Towards Deep Learning Models Resistant to Adversarial Attacks.International Conference on Learning Representations, 2018

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.893309Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07bc43bd-6766-420c-8712-d013f34b260d · outbound

This paper cites A Numerical Evaluation of Highly Accurate Multiple-Precision Arithmetic Version of Semidefinite Programming Solver: SDPA-GMP, -QD and -DD.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A Numerical Evaluation of Highly Accurate Multiple-Precision Arithmetic Version of Semidefinite Programming Solver: SDPA-GMP, -QD and -DD

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.872218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dad83c6d-ae6b-485a-9304-c9beeea7b372 · outbound

This paper cites California Institute of Technology, 2000.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification California Institute of Technology, 2000

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.854971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2cff32e-1fe0-4085-bef1-850259bec8cd · outbound

This paper cites Partial Facial Reduction: Simplified, Equivalent SDPs via Approximations of the PSD Cone.Mathematical Programming, 171:1–54, 2018.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Partial Facial Reduction: Simplified, Equivalent SDPs via Approximations of the PSD Cone.Mathematical Programming, 171:1–54, 2018

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.837184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0cf1a5f8-a0e8-40fc-9be4-e5dca83a7489 · outbound

This paper cites Semidefinite Relaxations for Certifying Robustness to Adversarial Examples.Advances in Neural Information Processing Systems, 31, 2018.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Semidefinite Relaxations for Certifying Robustness to Adversarial Examples.Advances in Neural Information Processing Systems, 31, 2018

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.821142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9fddd7e1-a345-4186-ae4d-7a845a920c67 · outbound

This paper cites A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks.Advances in Neural Information Processing Systems, 32, 2019.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks.Advances in Neural Information Processing Systems, 32, 2019

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.804123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e6d0ab6f-5082-4e2d-a785-e4f24b783be5 · outbound

This paper cites Perturbation Analysis of Singular Semidefinite Programs and Its Applica- tions to Control Problems.Journal of Optimization Theory and Applications, 188:52–72, 2021.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Perturbation Analysis of Singular Semidefinite Programs and Its Applica- tions to Control Problems.Journal of Optimization Theory and Applications, 188:52–72, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.786259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69866369-8374-4de4-ab8f-aa23b8823ed4 · outbound

This paper cites An Abstract Domain for Certifying Neural Networks.Proceedings of the ACM on Programming Languages, 3(POPL):1–30, 2019.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification An Abstract Domain for Certifying Neural Networks.Proceedings of the ACM on Programming Languages, 3(POPL):1–30, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.763559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f806c7a-7ed9-4751-8475-9ebb571273f1 · outbound

This paper cites SDPNAL+: A MATLAB Software for Semidefinite Programming With Bound Constraints (Version 1.0).Optimization Methods and Software, 35(1):87– 115, 2020.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification SDPNAL+: A MATLAB Software for Semidefinite Programming With Bound Constraints (Version 1.0).Optimization Methods and Software, 35(1):87– 115, 2020

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.739295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bebeeade-e7f3-48a8-9d6a-332c9fd7a3b7 · outbound

This paper cites Intriguing Properties of Neural Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Intriguing Properties of Neural Networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.720651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation daa68dd5-2cc1-4f84-b4e2-40b133d3c69f · outbound

This paper cites Evaluating Robustness of Neural Networks with Mixed Integer Programming.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Evaluating Robustness of Neural Networks with Mixed Integer Programming

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:00.222117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d74474e-6849-43d1-b860-0b4e905dcc41 · outbound

This paper cites Practical First-Order Methods for Large-Scale Semidefinite Programming.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Practical First-Order Methods for Large-Scale Semidefinite Programming

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.702933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abb6b4cd-6727-4786-95da-51ceead8cc43 · outbound

This paper cites Facial Reduction Algorithms for Conic Optimization Problems.Journal of Optimization Theory and Applications, 158:188–215, 2013.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Facial Reduction Algorithms for Conic Optimization Problems.Journal of Optimization Theory and Applications, 158:188–215, 2013

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.684453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a56511a0-64c5-4a03-b37b-e3e262412d8e · outbound

This paper cites Efficient Formal Safety Analysis of Neural Networks.Advances in Neural Information Processing Systems, 31, 2018.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Efficient Formal Safety Analysis of Neural Networks.Advances in Neural Information Processing Systems, 31, 2018

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.667989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1a7724e-aa3a-4d1b-a44b-25f36d40093d · outbound

This paper cites Beta-CROWN: Efficient Bound Propagation With Per-Neuron Split Constraints for Neural Network Robustness Verification.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Beta-CROWN: Efficient Bound Propagation With Per-Neuron Split Constraints for Neural Network Robustness Verification

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.651601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 38a9f8b8-1e5f-44a8-9839-4da428399301 · outbound

This paper cites Towards Fast Computation of Certified Robustness for ReLU Networks.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Towards Fast Computation of Certified Robustness for ReLU Networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.636178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc37be23-195d-4d0a-894c-7fd31672b835 · outbound

This paper cites Provable Defenses Against Adversarial Examples via the Convex Outer Adversarial Polytope.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Provable Defenses Against Adversarial Examples via the Convex Outer Adversarial Polytope

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.619068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 44446fd4-c8c0-4e6b-a896-34bcc00491bb · outbound

This paper cites Numerical Optimization, 2006.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Numerical Optimization, 2006

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.599369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddb6873e-face-4d86-b577-30d337af9707 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:00.267928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 648ed773-874d-4e6c-9811-6d5ee88a4fe7 · outbound

This paper cites Fast and Com- plete: Enabling Complete Neural Network Verification With Rapid and Massively Parallel Incomplete Verifiers.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Fast and Com- plete: Enabling Complete Neural Network Verification With Rapid and Massively Parallel Incomplete Verifiers

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.580924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 19e61ad7-dc06-461e-bc1a-9a7974ce323a · outbound

This paper cites Latest Developments in the SDPA Family for Solving Large-Scale SDPs.Handbook on Semidefinite, Conic and Polynomial Optimization, pages 687–713, 2012.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Latest Developments in the SDPA Family for Solving Large-Scale SDPs.Handbook on Semidefinite, Conic and Polynomial Optimization, pages 687–713, 2012

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.561946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 648fa9d3-d246-4973-95d0-bf743b94fde4 · outbound

This paper cites A High-Performance Software Package for Semidefinite Programs: SDPA 7.Handbook on Semidefinite, Conic and Polynomial Optimization, 2010.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification A High-Performance Software Package for Semidefinite Programs: SDPA 7.Handbook on Semidefinite, Conic and Polynomial Optimization, 2010

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.540530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c618eda-7e14-40c1-8bdf-c1f59411fde4 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification ADADELTA: An Adaptive Learning Rate Method

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:00.290137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bfe577f-c022-4008-8869-d1118f3500fe · outbound

This paper cites Scalable Neural Network Verification With Branch-and-Bound Inferred Cutting Planes.Advances in Neural Information Processing Systems, 2024.

Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification Scalable Neural Network Verification With Branch-and-Bound Inferred Cutting Planes.Advances in Neural Information Processing Systems, 2024

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:00.513620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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