Uploaded October 2025 | Updated September 2026, 2 weeks ago
This tech talk introduces the concept of formal verification for neural networks using intuitive examples and MATLAB® tools. It explains two main verification approaches: the polyhedra method, which uses linear inequalities to define boundaries for the feasible input and output spaces but can be computationally expensive, and the interval method, which is computationally simpler but more conservative due to overestimation of bounds.
Modern algorithms such as DeepPoly and CROWN improve interval methods by propagating constraints backward through the network, tightening bounds, and reducing conservatism. Deep Learning Toolbox™ supports these techniques, enabling practical verification of complex networks to ensure robust and safe operation in critical systems.
Related Resources:
- Check out this MATLAB script to verify network robustness: github.com/aerojunkie/control-tools/blob/master/FormalVerificationTechTalkMaterials/verify_robustness.m
- DeepPoly Reference: An Abstract Domain for Certifying Neural Networks: ggndpsngh.github.io/files/DeepPoly.pdf
- CROWN Reference: Efficient Neural Network Robustness Certification with General Activation Functions: arxiv.org/pdf/1811.00866
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© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
This tech talk introduces the concept of formal verification for neural networks using intuitive examples and MATLAB® tools. It explains two main verification approaches: the polyhedra method, which uses linear inequalities to define boundaries for the feasible input and output spaces but can be computationally expensive, and the interval method, which is computationally simpler but more conservative due to overestimation of bounds.
Modern algorithms such as DeepPoly and CROWN improve interval methods by propagating constraints backward through the network, tightening bounds, and reducing conservatism. Deep Learning Toolbox™ supports these techniques, enabling practical verification of complex networks to ensure robust and safe operation in critical systems.
Related Resources:
- Check out this MATLAB script to verify network robustness: github.com/aerojunkie/control-tools/blob/master/FormalVerificationTechTalkMaterials/verify_robustness.m
- DeepPoly Reference: An Abstract Domain for Certifying Neural Networks: ggndpsngh.github.io/files/DeepPoly.pdf
- CROWN Reference: Efficient Neural Network Robustness Certification with General Activation Functions: arxiv.org/pdf/1811.00866
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Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
