arXiv cs.AI· Mario Koddenbrock, Christoph Lange, Robin Legner, Martin J\"ager, Martin K\"ogler, Mariano N. Cruz Bournazou, Peter Neubauer, Felix Biessmann, Erik Rodner·· 12 小时前AI 评分31
RamanBench：拉曼光谱机器学习大规模基准发布
RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy
AI 导读
RamanBench 是首个面向拉曼光谱机器学习的大规模可复现基准，统一 77 个数据集、325,862 条光谱，覆盖四个领域的分类与回归任务。研究在标准化协议下评测 28 个模型，发现表格基础模型（如 TabPFN）持续优于领域专用模型和梯度提升基线，时间序列模型也具竞争力，但没有任何单一方法能在所有异构数据集上占优。
正文 · 原文
Abstract:Machine learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access (this https URL), evaluation protocols and code (this https URL), as well as a live leaderboard (this https URL). It unifies 77 datasets (including 17 first released with this benchmark) across four domains, comprising 325,862 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), tabular foundation models (TFMs; e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFMs consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no single method dominates across these heterogeneous datasets, revealing that Raman spectroscopy remains an open challenge for machine learning. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.
| Comments: | this https URL |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.02003 [cs.LG] |
| (or arXiv:2605.02003v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.02003 arXiv-issued DOI via DataCite |
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| Journal reference: | NeurIPS 2026 |
Submission history
From: Mario Koddenbrock [view email]
[v1]
Sun, 3 May 2026 18:12:42 UTC (4,736 KB)
[v2]
Wed, 6 May 2026 11:03:18 UTC (4,680 KB)
[v3]
Wed, 7 Oct 2026 12:10:59 UTC (4,720 KB)
[v4]
Fri, 16 Oct 2026 13:36:03 UTC (4,724 KB)
来源：arXiv cs.AI · arxiv.org