Performance Improvement
Performance improvement in various machine learning applications is a central research theme, focusing on enhancing model accuracy, efficiency, and robustness. Current efforts explore diverse strategies, including novel loss functions (e.g., for imbalanced datasets), optimized architectures (like wavelet-based networks and attention mechanisms), and innovative training techniques such as federated learning and adversarial training with parameter efficiency. These advancements have significant implications across diverse fields, from medical image analysis and drug discovery to recommendation systems and natural language processing, ultimately leading to more accurate, efficient, and reliable AI systems.
Papers
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach
Seyed Amir Hossein Aqajari, Ziyu Wang, Ali Tazarv, Sina Labbaf, Salar Jafarlou, Brenda Nguyen, Nikil Dutt, Marco Levorato, Amir M. Rahmani
ARCO:Adaptive Multi-Agent Reinforcement Learning-Based Hardware/Software Co-Optimization Compiler for Improved Performance in DNN Accelerator Design
Arya Fayyazi, Mehdi Kamal, Massoud Pedram