System Performance
System performance research focuses on optimizing the efficiency and accuracy of various computational systems, from machine learning models to robotic controllers and even quantum computers. Current research emphasizes improving model architectures (e.g., graph-oriented databases for language models, retention-based networks for multi-agent reinforcement learning) and training techniques (e.g., hard sample mining, co-optimization of design and control), while also addressing issues like fairness, robustness, and explainability. These advancements have significant implications for diverse fields, impacting the development of more efficient and reliable AI systems, improved medical diagnostics, and enhanced manufacturing processes.
Papers
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System
Tae Jin Park, He Huang, Ante Jukic, Kunal Dhawan, Krishna C. Puvvada, Nithin Koluguri, Nikolay Karpov, Aleksandr Laptev, Jagadeesh Balam, Boris Ginsburg
Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning
Soheila Farokhi, Aswani Yaramala, Jiangtao Huang, Muhammad F. A. Khan, Xiaojun Qi, Hamid Karimi
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score-Softmax Classifier
Cong Duan, Zixuan Liu, Jiahao Xia, Minghai Zhang, Jiacai Liao, Libo Cao
Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration
Ercong Nie, Helmut Schmid, Hinrich Schütze
On the Performance of Multimodal Language Models
Utsav Garg, Erhan Bas
COVID-19 South African Vaccine Hesitancy Models Show Boost in Performance Upon Fine-Tuning on M-pox Tweets
Nicholas Perikli, Srimoy Bhattacharya, Blessing Ogbuokiri, Zahra Movahedi Nia, Benjamin Lieberman, Nidhi Tripathi, Salah-Eddine Dahbi, Finn Stevenson, Nicola Bragazzi, Jude Kong, Bruce Mellado