Benchmark Suite

Benchmark suites are collections of standardized datasets and evaluation protocols designed to rigorously assess the performance of machine learning models across diverse tasks. Current research focuses on developing comprehensive suites for various domains, including video understanding, log analysis, compiler autotuning, and natural language processing, often evaluating large language models and other deep learning architectures. These suites are crucial for fostering reproducible research, enabling fair comparisons of different models and algorithms, and ultimately driving progress in the development of more robust and reliable AI systems with improved generalization capabilities across diverse real-world applications.

Papers

June 14, 2024