A multi-stage teacher forcing training method lets deep learning models outperform ECMWF-S2S forecasts at two to six weeks, reversing ChaosBench's finding that direct forecasting beats rolling.
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
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Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization
A multi-stage teacher forcing training method lets deep learning models outperform ECMWF-S2S forecasts at two to six weeks, reversing ChaosBench's finding that direct forecasting beats rolling.