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
Impact of Video Compression on the Performance of Object Detection Systems for Surveillance Applications
Michael O'Byrne, Vibhoothi, Mark Sugrue, Anil Kokaram
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, Sayan Ranu
VieCap4H-VLSP 2021: ObjectAoA-Enhancing performance of Object Relation Transformer with Attention on Attention for Vietnamese image captioning
Nghia Hieu Nguyen, Duong T. D. Vo, Minh-Quan Ha
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models
Hao Liu, Xinyang Geng, Lisa Lee, Igor Mordatch, Sergey Levine, Sharan Narang, Pieter Abbeel
Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data
Bhushan Chaudhari, Akash Agarwal, Tanmoy Bhowmik