Hugging Face Trending Papers
ChronoSheaf: Joint Spatio-Temporal Spectral Filtering on Cellular Sheaves
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arXiv Machine Learning
Oct 16
ChronoSheaf: Joint Spatio-Temporal Spectral Filtering on Cellular Sheaves
ChronoSheaf introduces a joint spatio-temporal spectral filtering framework for cellular sheaves, extending sheaf neural networks beyond instantaneous spatial operators. It defines a spectral sheaf Laplacian that simultaneously governs signal propagation across both graph structure and time, and employs a Dual Spectral Filter to broaden receptive fields without enlarging the sheaf. The model incorporates Transitional Restriction Maps to allow asymmetric temporal influence and demonstrates superior performance on various spatio-temporal forecasting benchmarks.
arXiv Machine Learning
Aug 4
Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling
arXiv:2608. 01318v1 Announce Type: cross Abstract: Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure.
Hugging Face Trending Papers
Aug 2
Sheaf-theoretic Signal Processing on Graphs: Spectral Theory, Filtering, and Sampling
Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geometric structure. Processing such data requires a mathematical framework capable of simultaneously modeling heterogeneous local signal spaces and the transformations relating them.
arXiv AI
Jun 9
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
arXiv:2606. 07695v1 Announce Type: cross Abstract: Multi-Modality Spatio-Temporal Forecasting (MoSTF) extends traditional spatio-temporal forecasting by incorporating diverse traffic modalities.
arXiv AI
1d ago
Consistent Geometric Deep Learning via Hilbert Bundles and Cellular Sheaves
arXiv:2605.06395v3 Announce Type: replace-cross Abstract: Modern deep learning architectures increasingly contend with sophisticated signals that are natively infinite-dimensional, such as time serie...
arXiv AI
Jun 15
MP3: Multi-Period Pattern Pre-training for Spatio-Temporal Forecasting
arXiv:2606. 13119v2 Announce Type: replace-cross Abstract: Spatio-Temporal forecasting is crucial in diverse fields, such as transportation, climate, and energy.