12 citations · 18 across the 6 of their papers we have counts for
6 papers
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
Jiaqi Xue, Mengxin Zheng, Ting Hua +4
Large Language Models (LLMs) are progressively being utilized as machine learning services and interface tools for various applications. However, the security implications of LLMs,…
Waterberry Farms: A Novel Benchmark For Informative Path Planning
Samuel Matloob, Partha P. Datta, O. Patrick Kreidl +3
Recent developments in robotic and sensor hardware make data collection with mobile robots (ground or aerial) feasible and affordable to a wide population of users. The newly emerg…
Predicting infections in the Covid-19 pandemic -- lessons learned
Sharare Zehtabian, Siavash Khodadadeh, Damla Turgut +1
Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the p…
Privacy-Preserving Learning of Human Activity Predictors in Smart Environments
Sharare Zehtabian, Siavash Khodadadeh, Ladislau Bölöni +1
The daily activities performed by a disabled or elderly person can be monitored by a smart environment, and the acquired data can be used to learn a predictive model of user behavi…
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
Siavash Khodadadeh, Sharare Zehtabian, Saeed Vahidian +3
Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, cluste…
Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
Hassam Ullah Sheikh, Ladislau Bölöni
The classic DQN algorithm is limited by the overestimation bias of the learned Q-function. Subsequent algorithms have proposed techniques to reduce this problem, without fully elim…