Results 121 to 130 of about 3,499,798 (281)

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

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors

Mathematics
Adaptive adversarial attacks, where adversaries tailor their strategies with full knowledge of defense mechanisms, pose significant challenges to the robustness of adversarial detectors. In this paper, we introduce RADAR (Robust Adversarial Detection via
Raz Lapid, Almog Dubin, Moshe Sipper
doaj   +1 more source

Prostate MR Image Segmentation With Self-Attention Adversarial Training Based on Wasserstein Distance

IEEE Access, 2019
Prostate diseases are very common in men. Accurate segmentation of the prostate plays a significant role in further clinical treatment and diagnosis. There have been some methods that combine the segmentation network and generative adversarial network ...
Chengwei Su, Renxiang Huang, Chang Liu, Tailang Yin, Bo Du   +4 more
doaj   +1 more source

Adversarial Training with Voronoi Constraints

CoRR, 2019
Adversarial examples are a pervasive phenomenon of machine learning models where seemingly imperceptible perturbations to the input lead to misclassifications for otherwise statistically accurate models. We propose a geometric framework, drawing on tools from the manifold reconstruction literature, to analyze the high-dimensional geometry of ...
Marc Khoury, Dylan Hadfield-Menell
openaire   +2 more sources

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, Marathe, Madhav V., Ravi, S.S.   +2 more
core  

Adversarial training-based robust model for transmission line’s insulator defect classification against cyber-attacks

The increased sophistication of smart grids has generated significant interest in employing unmanned aerial vehicles (UAVs) to monitor the operational condition of insulators, especially in identifying insulator defects to avoid substantial power loss ...
Islam, Rashidul, Hossain, Md Alamgi, Hossain, Jahangir, Rahman, Md. Abdur, Toni, Uttam MTonihapatra, Sheikh, Md. Rafiqul Islam   +5 more
core   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

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, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Improving Transferability of Physical Adversarial Attacks on Object Detectors Through Multi-Model Optimization

Applied Sciences
Physical adversarial attacks face significant challenges in achieving transferability across different object detection models, especially in real-world conditions.
Adonisz Dimitriu, Tamás Vilmos Michaletzky, Viktor Remeli   +2 more
doaj   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

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