Self-correcting Neural Networks for Safe Classification

[1]  Matt Fredrikson,et al.  Relaxing Local Robustness , 2021, NeurIPS.

[2]  J. Z. Kolter,et al.  Orthogonalizing Convolutional Layers with the Cayley Transform , 2021, ICLR.

[3]  Matthew Sotoudeh,et al.  Provable repair of deep neural networks , 2021, PLDI.

[4]  Quoc V. Le,et al.  EfficientNetV2: Smaller Models and Faster Training , 2021, ICML.

[5]  Matt Fredrikson,et al.  Globally-Robust Neural Networks , 2021, ICML.

[6]  Priya L. Donti,et al.  Enforcing robust control guarantees within neural network policies , 2020, ICLR.

[7]  Martin T. Vechev,et al.  Scaling Polyhedral Neural Network Verification on GPUs , 2020, MLSys.

[8]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[9]  Corina S. Pasareanu,et al.  Parallelization Techniques for Verifying Neural Networks , 2020, 2020 Formal Methods in Computer Aided Design (FMCAD).

[10]  Caterina Urban,et al.  Perfectly parallel fairness certification of neural networks , 2019, Proc. ACM Program. Lang..

[11]  Suresh Jagannathan,et al.  Art: Abstraction Refinement-Guided Training for Provably Correct Neural Networks , 2019, 2020 Formal Methods in Computer Aided Design (FMCAD).

[12]  Cem Anil,et al.  Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks , 2019, NeurIPS.

[13]  Vivek Srikumar,et al.  A Logic-Driven Framework for Consistency of Neural Models , 2019, EMNLP.

[14]  Suresh Jagannathan,et al.  An inductive synthesis framework for verifiable reinforcement learning , 2019, PLDI.

[15]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[16]  Mislav Balunovic,et al.  DL2: Training and Querying Neural Networks with Logic , 2019, ICML.

[17]  Isil Dillig,et al.  Optimization and abstraction: a synergistic approach for analyzing neural network robustness , 2019, PLDI.

[18]  Timon Gehr,et al.  An abstract domain for certifying neural networks , 2019, Proc. ACM Program. Lang..

[19]  Mykel J. Kochenderfer,et al.  Deep Neural Network Compression for Aircraft Collision Avoidance Systems , 2018, Journal of Guidance, Control, and Dynamics.

[20]  Cem Anil,et al.  Sorting out Lipschitz function approximation , 2018, ICML.

[21]  Matthew Mirman,et al.  Differentiable Abstract Interpretation for Provably Robust Neural Networks , 2018, ICML.

[22]  Swarat Chaudhuri,et al.  AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation , 2018, 2018 IEEE Symposium on Security and Privacy (SP).

[23]  Pushmeet Kohli,et al.  A Dual Approach to Scalable Verification of Deep Networks , 2018, UAI.

[24]  Ufuk Topcu,et al.  Safe Reinforcement Learning via Shielding , 2017, AAAI.

[25]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[26]  Ufuk Topcu,et al.  Shield synthesis , 2017, Formal Methods Syst. Des..

[27]  Rüdiger Ehlers,et al.  Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks , 2017, ATVA.

[28]  Mykel J. Kochenderfer,et al.  Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.

[29]  Min Wu,et al.  Safety Verification of Deep Neural Networks , 2016, CAV.

[30]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[31]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jonathan P. How,et al.  Optimized Airborne Collision Avoidance , 2015 .

[33]  Fan Long,et al.  Automatic runtime error repair and containment via recovery shepherding , 2014, PLDI.

[34]  Martin C. Rinard,et al.  Bolt: on-demand infinite loop escape in unmodified binaries , 2012, OOPSLA '12.

[35]  Michael D. Ernst,et al.  Automatically patching errors in deployed software , 2009, SOSP '09.

[36]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[37]  Michael R. Clarkson,et al.  Hyperproperties , 2008, 2008 21st IEEE Computer Security Foundations Symposium.

[38]  Walter Guttmann,et al.  Variations on an Ordering Theme with Constraints , 2006, IFIP TCS.

[39]  Emery D. Berger,et al.  DieHard: probabilistic memory safety for unsafe languages , 2006, PLDI '06.

[40]  Yuanyuan Zhou,et al.  Rx: treating bugs as allergies---a safe method to survive software failures , 2005, SOSP '05.

[41]  Daniel M. Roy,et al.  Enhancing Server Availability and Security Through Failure-Oblivious Computing , 2004, OSDI.

[42]  Robert Nieuwenhuis,et al.  Practical Algorithms for Deciding Path Ordering Constraint Satisfaction , 2002, Inf. Comput..

[43]  Matthias Felleisen,et al.  Contracts for higher-order functions , 2002, ICFP '02.

[44]  Grigore Rosu,et al.  Monitoring programs using rewriting , 2001, Proceedings 16th Annual International Conference on Automated Software Engineering (ASE 2001).

[45]  Bertrand Meyer,et al.  Eiffel: The Language , 1991 .

[46]  Sartaj Sahni,et al.  Parallel Matrix and Graph Algorithms , 1981, SIAM J. Comput..