Results 131 to 140 of about 25,829 (292)

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source

On the Adversarial Robustness of Hand-Crafted Features and Their Role in Defending Adversarial Examples

IEEE Access
Deep Neural Networks (DNNs) have achieved tremendous success in various computer vision tasks but remain highly vulnerable to adversarial examples. To address this limitation, we investigate the inherent robustness of hand-crafted features and validate ...
Shuohan Xue, Lihui Wu, Charith Abhayaratne   +2 more
doaj   +1 more source

Adversarial Attacks against the Perception System of Autonomous Vehicles

, 2023
The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety.
Gao, Yuxing (author)
core  

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

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

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

ATVis: Understanding and diagnosing adversarial training processes through visual analytics

Visual Informatics
Adversarial training has emerged as a major strategy against adversarial perturbations in deep neural networks, which mitigates the issue of exploiting model vulnerabilities to generate incorrect predictions.
Fang Zhu, Xufei Zhu, Xumeng Wang, Yuxin Ma, Jieqiong Zhao   +4 more
doaj   +1 more source

The Pros and Cons of Adversarial Robustness

Proceedings of the 17th International Conference on Agents and Artificial Intelligence
Robustness is widely regarded as a fundamental problem in the analysis of machine learning (ML) models. Most often robustness equates with deciding the non-existence of adversarial examples, where adversarial examples denote situations where small changes on some inputs cause a change in the prediction.
Yacine Izza, João Marques-Silva 0001
openaire   +2 more sources

Adversarially robust transfer learning

CoRR, 2019
Transfer learning, in which a network is trained on one task and re-purposed on another, is often used to produce neural network classifiers when data is scarce or full-scale training is too costly. When the goal is to produce a model that is not only accurate but also adversarially robust, data scarcity and computational limitations become even more ...
Shafahi, Ali, Saadatpanah, Parsa, Zhu, Chen, Ghiasi, Amin, Studer, Christoph; id_orcid0000-0001-8950-6267, Jacobs, David, Goldstein, Tom   +6 more
openaire   +4 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