Results 141 to 150 of about 2,268,403 (339)

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

, 2022
Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli   +6 more
openalex   +1 more source

Performance–Robustness Tradeoffs in Adversarially Robust Control and Estimation

IEEE Transactions on Automatic Control
arXiv admin note: substantial text overlap with arXiv:2203 ...
Bruce D. Lee, Thomas T.C.K. Zhang, Hamed Hassani, Nikolai Matni   +3 more
openaire   +2 more sources

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

Holistic Adversarially Robust Pruning

Accepted by ICLR ...
Zhao, Qi, Wressnegger, Christian
openaire   +2 more sources

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks. [PDF]

Comput Methods Programs Biomed, 2023
Ma L, Liang L.
europepmc   +1 more source

On Evaluating Adversarial Robustness

, 2019
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contributing factor is the difficulty of
Carlini, Nicholas, Athalye, Anish, Papernot, Nicolas, Brendel, Wieland, Rauber, Jonas, Tsipras, Dimitris, Goodfellow, Ian, Madry, Aleksander, Kurakin, Alexey   +8 more
openaire   +2 more sources

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Towards Adversarial Robustness for Multi-Mode Data through Metric Learning. [PDF]

Sensors (Basel), 2023
Khan S, Chen JC, Liao WH, Chen CS.
europepmc   +1 more source