Results 111 to 120 of about 6,306,959 (200)

Open set recognition of radar specific emitter based on adversarial reciprocal point learning

Radar specific emitter identification (SEI) is a key technology in electromagnetic spectrum control. Although the emergence of deep learning has promoted the development of SEI, there are still many shortcomings in the current research results.
Zhang, Wenxu, An, Lin, Yang, Wencheng, Liu, Feiran, Zhao, Zhongkai   +4 more
core   +1 more source

EAT-C: Environment-Adversarial sub-Task Curriculum for RL

, 2023
Reinforcement learning (RL) is inefficient on long-horizon tasks due to sparse rewards and its policy can be fragile to slightly perturbed environments.
Jiang, J, Song, X, Ao, S, Zhou, T, Long, G, Zhang, C   +5 more
core  

Wasserstein Adversarial Robustness [PDF]

, 2020
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core  

Higher Education: Increased and Equitable Access to Quality Learning Opportunities

, 2019
Given that many developing countries seek to increase participation in Higher Education (HE), COL will work with Ministries of Education and HE Institutions to support capacity building including the development and implementation of Open and Distance ...
Commonwealth of Learning
core   +1 more source

Meta-IDS-GAN: Utilizing Meta Learning to Enhance GAN-based Adversarial Traffic Generation

This thesis proposes Meta-IDS-GAN, a novel adversarial attack framework that combines Generative Adversarial Networks (GAN) with Model-Agnostic Meta-Learning (MAML) to generate highly transferable and statistically realistic adversarial network traffic ...
Xu, Yufeng
core  

How to beat a Bayesian adversary

European Journal of Applied Mathematics
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in ...
Zihan Ding, Kexin Jin, Jonas Latz, Chenguang Liu   +3 more
doaj   +1 more source

A Systematic Review of Adversarial Machine Learning and Deep Learning Applications

Al-Iraqia Journal for Scientific Engineering Research
The review delves into creating an understandable framework for machine learning in robotics. It stresses the significance of machine learning in materials science and robotics highlighting how it can transform industries by boosting efficiency and ...
Tabarak Ali Abdalkareem, Khamis A. Zidan, A. S. Albahri   +2 more
doaj   +1 more source

Quantum Machine Learning for 6G Network Intelligence and Adversarial Threats [PDF]

Quantum computing has been a major priority for several nations and prominent institutions in their pursuit of a transformative breakthrough in the fields of computation and encryption.
Duong, Trung Q, Nguyen, Lan-Huong, Hwang, Ren-Hung, Canberk, Berk, Nguyen, Van-Linh   +4 more
core   +1 more source

Adversarial Risk Analysis: The Somali Pirates case [PDF]

, 2013
Some of the current world’s biggest problems revolve around security issues. This has raised recent interest in resource allocation models to manage security threats, from terrorism to organized crime through money laundering.
Ríos, Jesús, Ríos Insúa, David
core  

Enhancing Machine Learning Security: The Significance of Realistic Adversarial Examples [PDF]

Adversarial attacks pose a significant security threat in Machine Learning (ML), employing subtle, invisible perturbations on original examples to craft instances that deceive model decisions.
DYRMISHI, Salijona
core   +1 more source