Matthew Mirman

According to our database, Matthew Mirman authored at least 14 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Ten simple rules for using large language models in science, version 1.0.

Gabriel Reuben Smith, Carolina Bello, Lalasia Bialic-Murphy, Emily Clark, Camille S. Delavaux, Camille Fournier de Lauriere, Johan van den Hoogen, Thomas Lauber, Haozhi Ma, Daniel S. Maynard, Matthew Mirman, Lidong Mo, Dominic Rebindaine, Josephine Elena Reek, Leland K. Werden, Zhaofei Wu, Gayoung Yang, Qingzhou Zhao, Constantin M. Zohner, Thomas Ward Crowther

PLoS Comput. Biol., January, 2024

2023

LLM Guided Inductive Inference for Solving Compositional Problems.

Abhigya Sodani, Lauren Moos, Matthew Mirman

CoRR, 2023

2022

Certified Deep Learning: Verification and Training.

Matthew Mirman

PhD thesis, 2022

The Fundamental Limits of Neural Networks for Interval Certified Robustness.

Matthew Mirman, Maximilian Baader, Martin T. Vechev

Trans. Mach. Learn. Res., 2022

2021

The Fundamental Limits of Interval Arithmetic for Neural Networks.

Matthew Mirman, Maximilian Baader, Martin T. Vechev

CoRR, 2021

Robustness certification with generative models.

Matthew Mirman, Alexander Hägele, Pavol Bielik, Timon Gehr, Martin T. Vechev

Proceedings of the PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021

2020

Robustness Certification of Generative Models.

Matthew Mirman, Timon Gehr, Martin T. Vechev

CoRR, 2020

Universal Approximation with Certified Networks.

Maximilian Baader, Matthew Mirman, Martin T. Vechev

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Online Robustness Training for Deep Reinforcement Learning.

Marc Fischer, Matthew Mirman, Steven Stalder, Martin T. Vechev

CoRR, 2019

A Provable Defense for Deep Residual Networks.

Matthew Mirman, Gagandeep Singh, Martin T. Vechev

CoRR, 2019

2018

AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation.

Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin T. Vechev

Proceedings of the 2018 IEEE Symposium on Security and Privacy, 2018

Fast and Effective Robustness Certification.

Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin T. Vechev

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentiable Abstract Interpretation for Provably Robust Neural Networks.

Matthew Mirman, Timon Gehr, Martin T. Vechev

Proceedings of the 35th International Conference on Machine Learning, 2018

Training Neural Machines with Trace-Based Supervision.

Matthew Mirman, Dimitar K. Dimitrov, Pavle Djordjevic, Timon Gehr, Martin T. Vechev

Proceedings of the 35th International Conference on Machine Learning, 2018