QLIF-CAST uses single-qubit quantum states to simulate leaky integrate-and-fire spiking dynamics in a recurrent architecture, achieving 15.4% lower MSE than classical LIF and up to 94% faster convergence than QLSTM on weather and air quality benchmarks.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
MAG-Net integrates radar dynamics with satellite IR, WV, and BTD channels via dual-stream encoding and uncertainty-weighted decoding to raise CSI40 by 0.083 over prior baselines for intense convective events.
citing papers explorer
-
QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting
QLIF-CAST uses single-qubit quantum states to simulate leaky integrate-and-fire spiking dynamics in a recurrent architecture, achieving 15.4% lower MSE than classical LIF and up to 94% faster convergence than QLSTM on weather and air quality benchmarks.
-
MAG-Net: Physics-Aware Multi-Modal Fusion of Geostationary Satellite and Radar for Severe Convective Precipitation Nowcasting
MAG-Net integrates radar dynamics with satellite IR, WV, and BTD channels via dual-stream encoding and uncertainty-weighted decoding to raise CSI40 by 0.083 over prior baselines for intense convective events.