Results 81 to 90 of about 2,268,403 (339)

Defense Architecture for Adversarial Examples of Ensemble Model Traffic Based on FeatureDifference Selection [PDF]

Jisuanji kexue
Currently,anomaly traffic detection models that leverage deep learning technologies are increasingly vulnerable to adversarial example attacks.Adversarial training has emerged as a potent defense mechanism against these adversarial attacks.By ...
HE Yuankang, MA Hailong, HU Tao, JIANG Yiming
doaj   +1 more source

Adversarial Robustness for Code

Proceedings of Machine Learning Research, 119, 2020
Proceedings of the 37th International Conference on Machine ...
Bielik, Pavol, Vechev, Martin
openaire   +3 more sources

Provable Tradeoffs in Adversarially Robust Classification

IEEE Transactions on Information Theory, 2023
This work has been submitted to the IEEE for possible publication.
Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey   +3 more
openaire   +2 more sources

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

Advanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng, Xin Qin, Jiahao Zhang, Wuliang Huang, Yiyang Zhang, Wentao Cui, Yao Chen, Shirui Li, Wenhao Liu, Yao Tian, Yana Liu, Jingxi Dong, Ping Xu, Zhenpeng Man, Guole Liu, Zhongming Liang, Xinlong Jiang, Xiaodong Yang, Pengfei Wang, Ge Yang, Hongmei Wang, Xuezhi Wang, Ming‐Han Tong, Yuanchun Zhou, Shihua Zhang, Yiqiang Chen, Yong Wang, Xin Li   +27 more
wiley   +1 more source

Manifold-driven decomposition for adversarial robustness

Frontiers in Computer Science
The adversarial risk of a machine learning model has been widely studied. Most previous studies assume that the data lie in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration.
Wenjia Zhang, Yikai Zhang, Xiaoling Hu, Yi Yao, Mayank Goswami, Chao Chen, Dimitris Metaxas   +6 more
doaj   +1 more source

Provably Robust Adversarial Examples

, 2020
International Conference on Learning Representations (ICLR 2022)
Dimitrov, Dimitar Iliev, Singh, Gagandeep, Gehr, Timon, Vechev, Martin   +3 more
openaire   +3 more sources

Unpaired Learning‐Enabled Nanotube Identification from AFM Images

Advanced Science, EarlyView.
Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na, Soobin Park, Younsu Jung, Jinhwa Park, Jimin Hong, Jihyun Lee, Albert Kim, Bongjun Kim, Gyoujin Cho, Eunju Cha, Seung Hyun Song   +10 more
wiley   +1 more source

MTFM: Multi-Teacher Feature Matching for Cross-Dataset and Cross-Architecture Adversarial Robustness Transfer in Remote Sensing Applications

Remote Sensing
Remote sensing plays a critical role in environmental monitoring, land use analysis, and disaster response by enabling large-scale, data-driven observation of Earth’s surface.
Ravi Kumar Rogannagari, Kazi Aminul Islam   +1 more
doaj   +1 more source

Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Advanced Science, EarlyView.
We present Diffusion‐MRI‐based Estimation of Cortical Architecture via Machine Learning (DECAM), a deep‐learning framework for estimating primate brain cortical architecture optimized with best response constraint and cortical label vectors. Trained using macaque brain high‐resolution multi‐shell dMRI and histology data, DECAM generates high‐fidelity ...
Tianjia Zhu, Minhui Ouyang, Shufang Tan, Jianlin Guo, Ziqin Zhang, Xuan Liu, Risheng Liu, Hao Huang   +7 more
wiley   +1 more source

Adversarially Robust Kernel Smoothing

, 2021
We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy.
Zhu, Jia-Jie, Kouridi, Christina, Nemmour, Yassine, Schölkopf, Bernhard   +3 more
openaire   +4 more sources