arXiv AI
OmniSpectra: A Unified Foundation Model for Universal Spectra Representation Learning
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arXiv AI
Jun 30
Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA
arXiv:2606. 28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects.
arXiv Machine Learning
Oct 21
ORBIT: Autoregressive Time Series Generation via Next Frequency-Band Prediction
arXiv:2610.27000v1 Announce Type: new Abstract: Deep generative models provide an effective modern solution to alleviating the problem of data scarcity by synthesizing realistic time series. However,...
arXiv AI
Jun 10
Integral Field Unit Spectroscopy with One Fiber
arXiv:2606. 10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution.
arXiv Machine Learning
Jun 9
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
arXiv:2604. 09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument.
arXiv Computer Vision
Oct 28
LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift
LESSViT introduces a sensor‑flexible Vision Transformer for hyperspectral imagery that uses a low‑rank structured attention mechanism (LESS Attention) to efficiently model joint spatial‑spectral interactions. By reducing the complexity from ≠O(N^2C^2) to ≠O(rNC) and adding channel‑agnostic patch embedding with wavelength‑aware positional encoding, it supports flexible spectral inputs. A hyperspectral masked autoencoder (HyperMAE) with decoupled spatial‑spectral masking further enables robust pretraining, and experiments on the SpectralEarth benchmark show improved robustness to spectral shifts while maintaining competitive in‑distribution performance.
arXiv Machine Learning
Oct 7
Learnable Spectral Activations
arXiv:2610.07419v1 Announce Type: new Abstract: Implicit neural representations (INRs) are shaped by the spectral structure induced by their input encodings and activation functions. Existing methods...