Machine learning uncertainties with adversarial neural networks

Machine learning uncertainties with adversarial neural networks

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Using adve...

Full description

Bibliographic Details
Main Authors:	Englert, Christoph, Galler, Peter, Spannowsky, Michael, Harris, Philip Coleman
Other Authors:	Massachusetts Institute of Technology. Department of Physics
Format:	Article
Language:	English
Published:	Springer Berlin Heidelberg 2019
Online Access:	http://hdl.handle.net/1721.1/120361 https://orcid.org/0000-0001-8189-3741

Similar Items

Adversarial examples in neural networks
by: Lim, Ruihong
Published: (2024)

Gluon-initiated associated production boosts Higgs physics
by: Englert, Christoph, et al.
Published: (2014)

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
by: Everett, Michael, et al.
Published: (2021)

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
by: Everett, Michael F, et al.
Published: (2021)

Demystifying adversarial attacks on neural networks
by: Yip, Lionell En Zhi
Published: (2020)

Adversarial attack defences for neural network
by: Singh Kirath
Published: (2022)

Adversarial attack defenses for neural networks
by: Puah, Yi Hao
Published: (2024)

Adversarial robustness of Bayesian neural networks
by: Wicker, M
Published: (2021)

Enhancing adversarial robustness of deep neural networks
by: Zhang, Jeffrey,M. Eng.Massachusetts Institute of Technology.
Published: (2019)

Protecting neural networks from adversarial attacks
by: Tan, Bryan Bing Xing
Published: (2020)

Protecting neural networks from adversarial attacks
by: Yeow, Zhong Han
Published: (2024)

Generating adversarial examples with graph neural networks
by: Jaeckle, F, et al.
Published: (2021)

Neighbor-anchoring adversarial graph neural networks
by: Liu, Zemin, et al.
Published: (2023)

Protecting neural networks from adversarial attacks
by: Lim, Xin Yi
Published: (2024)

Protecting neural networks from adversarial attacks
by: Kwek, Jia Ying
Published: (2020)

Universal adversarial attacks on graph neural networks
by: Liao, Chang
Published: (2021)

Adversarial robustness certification for Bayesian neural networks
by: Wicker, M, et al.
Published: (2024)

On the adversarial robustness of Bayesian machine learning models
by: Blaas, AC
Published: (2021)

An assessment of uncertainty due to adversary mobility
by: Fulchino, Matthew T
Published: (2015)

Adversarial Learned Soups: neural network averaging for joint clean and robust performance
by: Huang, Brian
Published: (2023)

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz’96 Model
by: Gagne, DJ, et al.
Published: (2020)

Adversarial Robustness Guarantees for Random Deep Neural Networks
by: De Palma, Giacomo, et al.
Published: (2022)

Towards deep neural networks robust to adversarial examples
by: Matyasko, Alexander
Published: (2020)

Review of adversarial attacks and defenses on edge machine learning
by: Chua, Jim Sean
Published: (2024)

Fast inference of deep neural networks in FPGAs for particle physics
by: Han, Song, et al.
Published: (2020)

Exploring the vulnerabilities and enhancing the adversarial robustness of deep neural networks
by: Bai, Tao
Published: (2022)

Cryptography techniques to defend neural networks from adversarial attacks
by: Tan, Hong Meng
Published: (2024)

Improved methodology for evaluating adversarial robustness in deep neural networks
by: Lee, Kyungmi,(Computer scientist)Massachusetts Institute of Technology.
Published: (2020)

Neural stethoscopes: Unifying analytic, auxiliary and adversarial network probing
by: Fuchs, F, et al.
Published: (2018)

Machine learning in housing design : exploration of generative adversarial network in site plan / floorplan generation
by: Wu, Chaoyun,M. ArchMassachusetts Institute of Technology.
Published: (2021)

Towards certification of uncertainty calibration under adversarial attacks
by: Emde, C, et al.
Published: (2024)

Detecting adversarial samples for deep neural networks through mutation testing
by: Tan, Kye Yen
Published: (2020)

Studies on neural networks: Information propagation at initialisation and robustness to adversarial examples
by: Ughi, G
Published: (2022)

An adversarial training framework for mitigating algorithmic biases in clinical machine learning
by: Yang, J, et al.
Published: (2023)

Towards machine learning models robust to adversarial examples and backdoor attacks
by: Makelov, Aleksandar
Published: (2023)

Semi-Supervised Learning with Coevolutionary Generative Adversarial Networks
by: Toutouh, Jamal, et al.
Published: (2023)

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment
by: Perdue, GN, et al.
Published: (2018)

Di-Higgs final states augMT2ed-Selecting hh events at the high luminosity LHC
by: Barr, A, et al.
Published: (2014)

Machine Learning Topological Invariants with Neural Networks
by: Zhang, Pengfei, et al.
Published: (2018)

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
by: Schwalbe-Koda, Daniel, et al.
Published: (2022)