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Found 4 papers, 2 papers with code
Multimodal Pretraining of Medical Time Series and Notes
In downstream tasks, including in-hospital mortality prediction and phenotyping, our pretrained model outperforms baselines in settings where only a fraction of the data is labeled, emphasizing its ability to enhance ICU data analysis.
Growing Representation Learning
However, many of these methods do not have a detection method for new classes or make assumptions about the distribution of classes.
A block-random algorithm for learning on distributed, heterogeneous data
The parameters defining these layers are initialized using random values and are "learned" from data, typically using stochastic gradient descent based algorithms.
From Deep to Physics-Informed Learning of Turbulence: Diagnostics
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence.
