From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators
arXiv 2607.00320•c7121665913e9c36745956894300d8dca23aabbf43daf72f4ad83d1fd0edf0c1
Cover theoryFourier Neural OperatorsKalman filteringNavier–StokesPDEsRealPage','REITs'TVARalgorithmic housingfunction-countinggenomics applicationsgraphical modelsgroup-Lassolow-dimensional structuremultitask learningneural estimationoperator learningprocess-noise estimationrank-based losssample complexityseparable graphsshared sparsityspectral methodstime-seriesuncertainty quantificationvariational inference
Paper metadata
- arXiv ID
- 2607.00320
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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- Source ID
- arxiv_stat_ml
- Record identifier
- c7121665913e9c36745956894300d8dca23aabbf43daf72f4ad83d1fd0edf0c1
- Enrichment time
- 2026-07-02T07:24:01Z
- AI-assisted enrichment
- Yes
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