Results 31 to 40 of about 86,248 (260)

Adversarially Robust Neural Architectures

IEEE Transactions on Pattern Analysis and Machine Intelligence
13 pages, 5 figures, 8 ...
Minjing Dong, Yanxi Li, Yunhe Wang, Chang Xu   +3 more
openaire   +3 more sources

Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness

, 2022
Accepted by ECCV 2022 oral ...
Zhang, Chaoning, Zhang, Kang, Zhang, Chenshuang, Niu, Axi, Feng, Jiu, Yoo, Chang D., Kweon, In So   +6 more
openaire   +2 more sources

Adversarial Robustness: Softmax versus Openmax

, 2017
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications. However, it was discovered that machine learning models, including the best performing DNNs, suffer from a fundamental problem ...
Boult, Terrance E., Günther, Manuel, Rozsa, Andras   +2 more
core   +1 more source

Are Accuracy and Robustness Correlated?

, 2016
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors.
Boult, Terrance E., Günther, Manuel, Rozsa, Andras   +2 more
core   +1 more source

Learning Lipschitz Feedback Policies From Expert Demonstrations: Closed-Loop Guarantees, Robustness and Generalization

IEEE Open Journal of Control Systems, 2022
In this work, we propose a framework in which we use a Lipschitz-constrained loss minimization scheme to learn feedback control policies with guarantees on closed-loop stability, adversarial robustness, and generalization.
Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti   +2 more
doaj   +1 more source

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

Advanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong, Dohyean Kim, Jiyun Jeong, Taekyeong Kim, Soon‐sung Kwon, Junyoung Seo, Il Jeon, Im Doo Jung   +7 more
wiley   +1 more source

A Comparative Study on the Performance and Security Evaluation of Spiking Neural Networks

IEEE Access, 2022
The brain-inspired Spiking neural networks (SNN) claim to present advantages for visual classification tasks in terms of energy efficiency and inherent robustness.
Yanjie Li, Xiaoxin Cui, Yihao Zhou, Ying Li   +3 more
doaj   +1 more source

Exploring Robust Features for Improving Adversarial Robustness

IEEE Transactions on Cybernetics
While deep neural networks (DNNs) have revolutionized many fields, their fragility to carefully designed adversarial attacks impedes the usage of DNNs in safety-critical applications. In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i.e., invariant to the clean image and its adversarial ...
Hong Wang, Yuefan Deng, Shinjae Yoo, Yuewei Lin   +3 more
openaire   +3 more sources

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, EarlyView.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source