# A Case for Dataset Specific Profiling

**Authors:** Seth Ockerman, John Wu, Christopher Stewart  
**arXiv:** [2208.03315](https://arxiv.org/abs/2208.03315)  
**Date:** 2022-08-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.03315v1/

## Key points

- The emerging paradigm of data-driven science relies on computational AI models and discipline-specific datasets
- Benchmarking approaches have been used to infer performance without executing models
- Limitations of benchmarking include bias towards representative datasets and potential selection of subpar models
- A new dataset-aware benchmarking paradigm is needed
- Discipline-specific datasets significantly impact model performance compared to classical benchmarking datasets
- Dataset differences in "learning" are not limited to neural networks
- Discrepancy between different learning algorithms is more pronounced with discipline-specific datasets
- Lightweight model execution can improve benchmarking accuracy
- Dataset-specific profiling is important in selecting computational models for data-driven science

## Summary

The emerging paradigm of data-driven science relies on the execution of computational AI models against discipline-specific datasets to uncover hidden concepts and enable scientific applications. With modern machine learning frameworks, anyone can develop and execute these models. However, computing the performance of every computational model against important and widely used datasets is prohibitively expensive in terms of cloud resources. To address this challenge, benchmarking approaches have been used to infer performance without actually executing models. These approaches use representative datasets but limit extensive dataset profiling to only a few datasets. This introduces bias that favors models suited for representative datasets, leaving each dataset's unique characteristics unexplored and potentially selecting subpar models based on inference from generalized datasets. To overcome these limitations, a new paradigm is needed that incorporates dataset profiling into the model selection process. This necessitates answering two key questions: (1) Can scientific datasets significantly alter the rank order of computational models compared to widely used representative datasets? (2) If so, can lightweight model execution improve benchmarking accuracy? By addressing these questions, we can lay the foundation for a new dataset-aware benchmarking paradigm. The study presented in this paper demonstrates that discipline-specific datasets do indeed significantly impact model performance compared to classical benchmarking datasets. Additionally, it reveals that the complexity of discipline-specific datasets leads to similar limitations in benchmarking as observed with classical datasets. The paper also explores three key findings: (1) Dataset differences in "learning" are not limited to neural networks; (2) The discrepancy between different learning algorithms is more pronounced when using discipline-specific datasets; and (3) Lightweight model execution can provide valuable insights into benchmarking accuracy. In conclusion, this research highlights the importance of dataset-specific profiling in selecting computational models for data-driven science. It emphasizes the need to move beyond relying solely on representative datasets and instead consider the unique characteristics of each dataset. By doing so, we can ensure more accurate model selection and enable advancements in various scientific domains.