arXiv Machine Learning

RamanPFN: learning from Raman spectral structure with a tabular foundation model

arXiv:2608. 02157v1 Announce Type: new Abstract: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring.

Hugging Face Trending Papers

Aug 3

RamanPFN: learning from Raman spectral structure with a tabular foundation model

Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring. Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers, with informative variation within bands and across distant spectral regions.

arXiv Machine Learning

Jun 15

Machine Learning for Biomedical Raman Spectroscopy: From Spectral Acquisition to Clinical Translation

arXiv:2606. 14169v1 Announce Type: new Abstract: Raman spectroscopy provides label-free, chemically specific characterization of biological systems and has become an important tool for cancer diagnosis, molecular subtyping, microbiological identification, and intraoperative decision support.

By Bogdan Oancea, Ana Maria Seciu-Grama, Nicoleta Siminea, Laura Mihaela Stefan, Alice Stoica, Joel Sjoberg, Marian Necula, Ana-Maria Prelipcean, Corneliu Ovidiu Vrancianu, Eduard Milea, Andrei P\u{a}un, Ion Petre, Mihaela P\u{a}un

fine-tuning multimodal

arXiv Machine Learning

Jul 14

Generative Augmentation of Raman Spectra for Glioma Classification

arXiv:2607. 10196v1 Announce Type: new Abstract: Access to sufficiently large biomedical datasets remains a major obstacle for machine learning in Raman spectroscopy-based diagnostics.

By Andrei Iu\c{s}an, Iulian Vasile, Daria Voiculescu, Ion Petre, Andrei P\u{a}un, Bogdan Oancea, Mihaela P\u{a}un

arXiv Machine Learning

Sep 18

Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics

Sharpness-Aware Minimization (SAM) is applied to improve the generalization of machine learning models for classifying bacterial Raman spectra, a technique that could enable rapid, portable diagnostics for antimicrobial resistance. The study shows that SAM can increase classification accuracy by up to 10.5% on a single data split and by an average of 2.7% across multiple splits compared to the traditional Adam optimizer. These gains demonstrate SAM’s potential to enhance the clinical utility of AI-powered Raman spectroscopy tools.

By Kaitlin Zareno, Jarett Dewbury, Siamak K. Sorooshyari, Hossein Mobahi, Loza F. Tadesse

arXiv Machine Learning

Aug 4

From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

arXiv:2608. 00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.

By Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers, Martin Atzmueller

llms benchmarks

arXiv Machine Learning

2d ago

Molecular Property Prediction under Structural Shift with Tabular Foundation Models

arXiv:2609.38744v1 Announce Type: new Abstract: Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials de...

By Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo

llms

arXiv Machine Learning

Sep 15

Multimodal deep learning from spectra for small-molecule structure identification: enhancing robustness with mixed-condition training

arXiv:2609.14360v1 Announce Type: new Abstract: In practical molecular characterization, small-molecule structure identification benefits from complementary spectroscopic evidence, but missing, degra...

By Bowen Gao, Lei Zhu, Yiying Wang, Wenjie Yu

rag multimodal

arXiv Machine Learning

Jul 23

Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data

arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.

By Chengchun Liu, Zhiyuan Yan, Li Yuan, Hao Li, Boxuan Zhao, Yonghong Tian, Bartosz A. Grzybowski, Fanyang Mo

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 16

Spectral Analysis of Molecular Features: When Richer Features Do Not Guarantee Better Generalization

arXiv:2510. 14217v2 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality.

By Asma Jamali, Tin Sum Cheng, Rodrigo A. Vargas-Hern\'andez

llms rag benchmarks

arXiv Machine Learning

Jun 26

Transformer-Based Classification of Bacterial Raman Spectra with LOOCV

arXiv:2606. 27096v1 Announce Type: new Abstract: Transformer-based models have recently attracted increasing attention for Raman spectral classification.

By Jamile Mohammad Jafari, Thomas Bocklitz

llms

arXiv AI

Aug 14

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.

By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia

fine-tuning safety

arXiv AI

Jun 2

{\lambda}Split: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy

arXiv:2603. 23647v2 Announce Type: replace-cross Abstract: In fluorescence microscopy, spectral unmixing aims to recover individual fluorophore concentrations from spectral images that capture mixed fluorophore emissions.

By Federico Carrara, Talley Lambert, Mehdi Seifi, Florian Jug

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