Results 91 to 100 of about 6,306,959 (200)

Adversarial examples for extreme multilabel text classification

, 2022
Tallennetaan OA-artikkeli, kun julkaistuExtreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive labels, and (iii) the data follows a
Babbar, Rohit, Mohammadnia Qaraei, Mohammadreza   +1 more
core   +1 more source

Adversarial sample generation algorithm for vertical federated learning

Tongxin xuebao, 2023
To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was ...
Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG   +4 more
doaj   +2 more sources

Deep Learning and Music Adversaries [PDF]

, 2015
OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative.
STURM, BLT, Kereliuk, C, Kereliuk, Corey Mose, Sturm, Bob L., Larsen, Jan, Larsen, J   +5 more
core   +1 more source

Statistical Feature-Based Detection of Adversarial Noise and Patch Attacks in Image and Deepfake Analysis

195208Adversarial attacks pose a significant threat to the reliability and trustworthiness of machine learning systems, particularly in image classification tasks like deepfake detection.
Bunzel, Niklas, Göller, Nicolas, Steinebach, Martin, Graner, Lukas, Frick, Raphael   +4 more
core   +1 more source

Adversarial Risk Análysis for Counterterrorism Modelling [PDF]

, 2013
Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
core  

Breaking Machine Learning Models with Adversarial Attacks and its Variants

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Machine learning models can be by adversarial attacks, subtle, imperceptible perturbations to inputs that cause the model to produce erroneous outputs.
Pavan Reddy
doaj   +1 more source

Zero-Shot Learning by Harnessing Adversarial Samples

, 2023
Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the knowledge, i.e., visual and semantic relationships, obtained from seen classes, where image augmentation techniques are commonly applied to improve the generalization ability ...
Chen, Zhi, Wang, Sen, Huang, Zi, Zhang, Pengfei, Li, Jingjing   +4 more
core   +1 more source

Hierarchical Adversarially Learned Inference

CoRR, 2018
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing ...
Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro, Negar Rostamzadeh, Jovana Mitrovic, Aaron C. Courville   +5 more
openaire   +3 more sources

Multiple Graph Adversarial Learning

CoRR, 2019
Recently, Graph Convolutional Networks (GCNs) have been widely studied for graph-structured data representation and learning. However, in many real applications, data are coming with multiple graphs, and it is non-trivial to adapt GCNs to deal with data representation with multiple graph structures.
Bo Jiang 0002, Ziyan Zhang, Jin Tang 0001, Bin Luo 0001   +3 more
openaire   +2 more sources

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