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

• URL: http://arxiv.org/abs/2608.02157v1
• Date: Mon, 03 Aug 2026 12:37:55 GMT
• Title: RamanPFN: learning from Raman spectral structure with a tabular foundation model
• Abstract summary: Raman spectroscopy enables non-destructive, label-free molecular characterization across materials science, biomedicine and process monitoring.<n> Predictive Raman datasets often contain few labelled spectra and thousands of ordered wavenumbers.<n>We present RamanPFN, a spectral representation framework that encodes dependencies before TabPFN inference.
• Score: 5.156304764307903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Latent-variable chemometrics accommodates collinear small-sample data but can obscure fine peak morphology, whereas deep spectral networks resolve this structure only after task-specific training. TabPFN avoids task-specific parameter fitting through pretrained in-context inference, but processes very wide inputs as feature-subsampled views that do not preserve joint visibility of related bands. We present RamanPFN, a spectral representation framework that encodes these dependencies before TabPFN inference. Global Compositional Unmixing constructs non-negative coordinates over the complete spectrum so that distant bands with shared latent variation occupy a common predictive axis. Local Vibrational Subspace Encoding represents contiguous wavenumber regions with multiple orthogonal modes that retain independent changes in peak shape, intensity and position. The representations are evaluated separately and combined at the prediction level. Evaluation covered 150 tasks from 74 public Raman datasets. RamanPFN reduced root-mean-square error by 19.6% on average across 129 regression targets relative to direct TabPFN inference and further reduced the remaining classification error by 9.0% across 21 classification tasks. These results establish explicit spectral representation as an effective interface between high-dimensional Raman measurements and reusable tabular inference.

Related papers

• Spectral Outliers Reveal Dominant Learned Structure in Transformer Attention [0.8971132850029493]
  We apply Marchenko-Pastur (MP) random matrix theory to pre-trained attention weights in order to separate each projection matrix into a random-like bulk and a set of spectral outliers.<n>We validate this decomposition causally: zeroing the MP-identified outliers (signal) in Mistral-7B drives HellaSwag, MMLU, and PIQA close to random-chance performance.
  arXiv  Detail & Related papers  (2026-08-08T04:56:11Z)
• Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection [26.632380150006753]
  FressDet is a fully rotation-equivariant spectral-spatial learning framework for multispectral object detection.<n>It captures the continuous, ordered nature of spectral structure and enables reliable spectral-spatial fusion across pyramid levels.<n>FressDet learns more discriminative and robust spectral-spatial representations even under rotational perturbations.
  arXiv  Detail & Related papers  (2026-07-06T14:29:51Z)
• Full spectrum Unlearnable Examples via Spectral Equalization [29.22443621401928]
  We propose Full-spectrum Unlearnable examples via Spectral Equalization (FUSE)<n>FUSE generates spectrum-agnostic perturbations by equalizing the contributions from different bands and enforcing cross-band consistency.<n>Experiments across multiple datasets, architectures, and spectral filtering demonstrate the strong protection achieved by FUSE.
  arXiv  Detail & Related papers  (2026-06-25T07:53:53Z)
• Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations [0.0]
  Neural operator surrogates approximate PDE solutions orders of magnitude faster than numerical solvers.<n>Sparse sensor measurements of the field are often available too, offering pointwise accuracy without spectral distortion.<n>We address this by treating NO predictions as auxiliary observations in a diffusion posterior sampling framework.
  arXiv  Detail & Related papers  (2026-06-02T17:26:15Z)
• SEED: Targeted Data Selection by Weighted Independent Set [76.68391670109433]
  We develop a robust and scalable data selection pipeline dubbed SEED.<n>SEED consistently outperforms state-of-the-art methods on instruction tuning, visual instruction tuning, and semantic segmentation.
  arXiv  Detail & Related papers  (2026-05-15T07:26:54Z)
• FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time [52.735012862324766]
  We present FRIGID, a framework with a novel diffusion language model that generates structures conditioned on mass spectra.<n>We show how forward fragmentation models enable inference-time scaling by identifying spectrum-inconsistent fragments.<n>Further empirical analyses show that FRIGID exhibits log-linear performance scaling with increasing inference-time compute.
  arXiv  Detail & Related papers  (2026-04-17T19:11:18Z)
• StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models [57.674757786328236]
  StructSAM is a resolution-preserving merge-unmerge framework tailored to Segment Anything Model (SAM)<n>We show that StructSAM reduces encoder FLOPs by 25-30% (up to 40%+ with prompt-aware merging) with minor drops in mIoU/Dice.<n>We also provide a spectral graph coarsening view showing that score-guided merging yields bounded Laplacian spectral distortion compared to random or window-restricted baselines.
  arXiv  Detail & Related papers  (2026-03-07T18:30:58Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• Data-Driven Graph Filters via Adaptive Spectral Shaping [10.449640808601199]
  We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering.<n>The framework provides compact spectral modules that plug into graph signal processing pipelines and graph neural networks.
  arXiv  Detail & Related papers  (2026-02-03T16:20:49Z)
• LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation [55.800319453296886]
  Time series with missing or irregularly sampled data are a persistent challenge in machine learning.<n>We introduce a different Lombiable--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data.
  arXiv  Detail & Related papers  (2025-06-20T14:48:42Z)
• Hodge-Aware Contrastive Learning [101.56637264703058]
  Simplicial complexes prove effective in modeling data with multiway dependencies. We develop a contrastive self-supervised learning approach for processing simplicial data.
  arXiv  Detail & Related papers  (2023-09-14T00:40:07Z)
• Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data [0.0]
  We present a rotation-equivariant unsupervised learning framework for the sparse deconvolution of non-negative scalar fields defined on the unit sphere. We show improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.
  arXiv  Detail & Related papers  (2021-02-17T16:04:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.