OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
arXiv 2607.11896•44675fb57b8a9ce0d469043b6f1d40655da31b662fad4afa4def4fb7fd0db1d5
ConvCNPDurr-HoyerGaussian-processGaussian-set-convolutionKV-cache-compression','query-agnosticLoRAPM10VAEactivation-compressionair-qualitybattery-degradationchunk-WYdelta-attentionfringe-projection-profilometrygraph-neural-networkhyperdimensional-computinglinear-attentionlogarithmic-encodingphase-controlquantum-computingreflection-theorysensor-fusionspatial-attentionviable-path-entropywrapped-phase
Paper metadata
- arXiv ID
- 2607.11896
- Version
- Not specified by this published record
- Category
- Computer Science — Machine Learning (cs.LG)
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Evidence and limitations
- Source ID
- arxiv_cs_lg
- Record identifier
- 44675fb57b8a9ce0d469043b6f1d40655da31b662fad4afa4def4fb7fd0db1d5
- Enrichment time
- 2026-07-15T08:52:12Z
- AI-assisted enrichment
- Yes
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