Results 91 to 100 of about 85,147 (260)
AIChE Journal, EarlyView.Abstract This work experimentally validates the RESPONSE (Resilient Process cONtrol SystEm) framework as a solution for maintaining safe, continuous operation of cyber‐physical process systems under cyberattacks. RESPONSE implements a dual‐loop architecture that runs a networked online controller in parallel with a hard‐isolated offline controller ...
Luyang Liu +5 more
wiley +1 more source
ATVis: Understanding and diagnosing adversarial training processes through visual analytics
Visual InformaticsAdversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.
Fang Zhu +4 more
doaj +1 more source
Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks
IEEE Access, 2019 Adversarial examples are perturbed inputs that are designed (from a deep learning network's (DLN) parameter gradients) to mislead the DLN during test time.
Priyadarshini Panda +2 more
doaj +1 more source
Are Robust LLM Fingerprints Adversarially Robust?
Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly focused on benign perturbations such as incremental fine-tuning, model merging, and prompting. Lack of systematic investigations into {\em adversarial robustness} against a malicious model host leaves ...
Nasery, Anshul +4 more
openaire +2 more sources
Robust Generative Adversarial Network
, 2020 Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations. Most existing works focus on stabilizing the training of the discriminator while ignoring the generalization properties.
Zhang, Shufei +4 more
openaire +2 more sources
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz +2 more
wiley +1 more source
Adversarially robust transfer learning
, 2019 Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computational limitations become even more ...
Shafahi, Ali +6 more
openaire +3 more sources
Advanced Intelligent Discovery, EarlyView.A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali +5 more
wiley +1 more source
RobustCheck: A Python package for black-box robustness assessment of image classifiers
SoftwareXThe robustness of computer vision models against adversarial attacks is a critical matter in machine learning that is often overlooked by researchers and developers.
Andrei Ilie, Alin Stefanescu
doaj +1 more source