Results 101 to 110 of about 4,416 (262)

Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-Model Bounded Divergence Gating

IEEE Access
Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions ...
Hossein Aboutalebi, Mohammad Javad Shafiee, Chi-En Amy Tai, Alexander Wong   +3 more
doaj   +1 more source

Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification. [PDF]

Entropy (Basel), 2023
Da Q, Zhang G, Wang W, Zhao Y, Lu D, Li S, Lang D.   +6 more
europepmc   +1 more source

Factors influencing the nature of client complaint behaviour in the aftermath of adverse events

Veterinary Record, Volume 196, Issue 6, 15/22 March 2025.
Abstract Background Negative veterinary client complaint behaviour poses wellbeing and reputational risks. Adverse events are one source of complaint. Identifying factors that influence adverse event‐related complaint behaviour is key to mitigating detrimental consequences and harnessing information that can be used to improve service quality, patient ...
Julie Gibson, Kate White, Liz Mossop, Marnie L. Brennan   +3 more
wiley   +1 more source

Dictionary Learning Based Scheme for Adversarial Defense in Continuous-Variable Quantum Key Distribution. [PDF]

Entropy (Basel), 2023
Li S, Yin P, Zhou Z, Tang J, Huang D, Zhang L.   +5 more
europepmc   +1 more source

Nanomedicine applications in lymphoma: Advancing precision diagnostics, targeted therapeutics, and prospective developments

VIEW, EarlyView.
Lymphoma is a group of blood cancers that can appear in lymph nodes, blood, bone marrow, spleen, liver, or the central nervous system, which makes drug delivery and disease monitoring difficult. This review summarizes how nanomedicine technologies may improve targeted treatment and imaging, while carefully separating approved or guideline‐supported ...
Mohd Ahmar Rauf, Yara Mashal, Afeefa Rao, Nasar Ata, Arun K. Iyer, Farhan Shah   +5 more
wiley   +1 more source

Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection. [PDF]

Heliyon, 2022
Riza Rizky LM, Suyanto S.
europepmc   +1 more source

Understanding and Mitigating Bias From Artificial Intelligence in Otolaryngology: A State‐of‐the‐Art Review

World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.
ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley   +1 more source

Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling. [PDF]

Sensors (Basel), 2022
Kim M, Joo S.
europepmc   +1 more source

Defensive Distillation is Not Robust to Adversarial Examples

CoRR, 2016
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Nicholas Carlini, David A. Wagner 0001
openaire   +3 more sources

Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu, Xueqiang Gu, Caiming Zheng, Xin Xu, Zhangling Wang, Wanpeng Zhang, Yixing Lan   +6 more
wiley   +1 more source