31 citations · 43 across the 9 of their papers we have counts for
9 papers
Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets
Ximeng Liu, Qianlong Wang, Yingming Mao +6
LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experi…
Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Ximeng Liu, Zhuoran Liu, Yingming Mao +3
Recently, researchers have explored ML-based Traffic Engineering (TE), leveraging neural networks to solve TE problems traditionally addressed by optimization. However, existing ML…
Evaluatology: The Science and Engineering of Evaluation
Jianfeng Zhan, Lei Wang, Wanling Gao +10
Evaluation is a crucial aspect of human existence and plays a vital role in various fields. However, it is often approached in an empirical and ad-hoc manner, lacking consensus on…
SAIBench: A Structural Interpretation of AI for Science Through Benchmarks
Yatao Li, Jianfeng Zhan
Artificial Intelligence for Science (AI4S) is an emerging research field that utilizes machine learning advancements to tackle complex scientific computational issues, aiming to en…
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
Yatao Li, Wanling Gao, Lei Wang +3
AI for science (AI4S) is an emerging research field that aims to enhance the accuracy and speed of scientific computing tasks using machine learning methods. Traditional AI benchma…
SAIBench: Benchmarking AI for Science
Yatao Li, Jianfeng Zhan
Scientific research communities are embracing AI-based solutions to target tractable scientific tasks and improve research workflows. However, the development and evaluation of suc…
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
Fei Tang, Wanling Gao, Jianfeng Zhan +30
Earlier-stage evaluations of a new AI architecture/system need affordable benchmarks. Only using a few AI component benchmarks like MLPerfalone in the other stages may lead to misl…
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
Wanling Gao, Fei Tang, Jianfeng Zhan +31
Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process…
AIBench: An Industry Standard Internet Service AI Benchmark Suite
Wanling Gao, Fei Tang, Lei Wang +22
Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many inn…