Results 141 to 150 of about 25,829 (292)

Wavelets Beat Monkeys at Adversarial Robustness

, 2023
Research on improving the robustness of neural networks to adversarial noise - imperceptible malicious perturbations of the data - has received significant attention.
Kempe, Julia, Su, Jingtong
core  

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

Adversarial Robustness of Nonparametric Regression

CoRR
25 pages, 6 ...
Parsa Moradi, Hanzaleh Akbarinodehi, Mohammad Ali Maddah-Ali   +2 more
openaire   +2 more sources

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Adversarial Robustness in Quantum Machine Learning: A Scoping Review

Computers
Quantum machine learning (QML) is emerging as a promising paradigm at the intersection of quantum computing and artificial intelligence, yet its security under adversarial conditions remains insufficiently understood.
Yanche Ari Kustiawan, Khairil Imran Ghauth   +1 more
doaj   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Active machine learning approach to adversarial training improves trade-off between natural accuracy and adversarial robustness

Scientific Reports
Understanding our world which is open and diverse requires foundation models that generalize well while trustworthy. Adversarial training has been considered to be one of the most effective strategies to achieve robust learning systems, yet adversarial ...
Seyed Mohammad Hadi Mirsadeghi
doaj   +1 more source

RobustCheck: A Python package for black-box robustness assessment of image classifiers

SoftwareX
The 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

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source