Results 141 to 150 of about 1,662,189 (295)
Experimental demonstration of adversarial examples in learning topological phases. [PDF]
Nat Commun, 2022 Zhang H +9 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images
Advanced Intelligent Discovery, EarlyView.A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski +5 more
wiley +1 more source
DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model. [PDF]
Front Neurorobot, 2023 Liu R +6 more
europepmc +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Explaining and Harnessing Adversarial Examples
, 2014 Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.
Ian J. Goodfellow +2 more
openaire +3 more sources
A Universal Detection Method for Adversarial Examples and Fake Images. [PDF]
Sensors (Basel), 2022 Lai J, Huo Y, Hou R, Wang X.
europepmc +1 more source
Large Language Model‐Based Chatbots in Higher Education
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion. [PDF]
In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel +2 more
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