4 papers
Mesh-RL: Coupled subgrid reinforcement learning
Behnam Gheshlaghi, Bahador Rashidi, Shahin Atakishiyev
Reinforcement learning in large or sparse-reward environments suffers from slow temporal-difference reward propagation, as value information spreads only locally across the state s…
Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
Shahin Atakishiyev, Housam K. B. Babiker, Jiayi Dai +8
Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next…
An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani +2
The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the ability to explain the beha…
Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
Shahin Atakishiyev, Mohammad Salameh, Randy Goebel
The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles (AVs), largely due to advances in deep learning, th…