Results 51 to 60 of about 4,313 (262)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

A Mask-Based Adversarial Defense Scheme

Algorithms, 2022
Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negative ...
Weizhen Xu, Chenyi Zhang, Fangzhen Zhao, Liangda Fang   +3 more
doaj   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Complex & Intelligent Systems
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model
Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang   +6 more
doaj   +1 more source

You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models

IEEE Access, 2021
Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang, Zuohui Chen, Hui Dong, Qi Xuan   +3 more
doaj   +1 more source

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

CoRR, 2017
We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.
Hyeungill Lee, Sungyeob Han, Jungwoo Lee 0001   +2 more
openaire   +2 more sources

Understanding and Improving Ensemble Adversarial Defense

Advances in Neural Information Processing Systems 36, 2023
The strategy of ensemble has become popular in adversarial defense, which trains multiple base classifiers to defend against adversarial attacks in a cooperative manner. Despite the empirical success, theoretical explanations on why an ensemble of adversarially trained classifiers is more robust than single ones remain unclear.
Deng, Yian, Mu, Tingting
openaire   +4 more sources

Navigating Ethical Minefields: Micro‐Foundations of Corporate Political Work in Ethical and Sustainable Business Performance

Business Strategy and the Environment, EarlyView.
ABSTRACT In an era of rising geopolitical tensions and environmental instability, corporate political activities have become increasingly intertwined with ethical challenges and sustainability requirements. This study investigates the influence of environmental dynamics and corporate ethical responsibility on interorganizational conflict and ...
David Yulong Liu, Justin Zuopeng Zhang, Jimmy Sun, Muhammad Mustafa Kamal, Bhumika Gupta   +4 more
wiley   +1 more source

Applying the Rules of Evidence to Expert Testimony About Risk

Behavioral Sciences &the Law, EarlyView.
ABSTRACT Expert opinion about dangerousness or risk is common at sentencing, criminal commitment proceedings and some types of pretrial detention hearings. This article argues that such evidence must be (1) “material” (logically relevant, empirically generalizable, and epistemologically germane), (2) “probative” (a measure of accuracy, which is ...
Christopher Slobogin
wiley   +1 more source

Adversarial defense based on distribution transfer

CoRR, 2023
The presence of adversarial examples poses a significant threat to deep learning models and their applications. Existing defense methods provide certain resilience against adversarial examples, but often suffer from decreased accuracy and generalization performance, making it challenging to achieve a trade-off between robustness and generalization.
Jiahao Chen, Diqun Yan, Li Dong 0006
openaire   +2 more sources