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Found 6 papers, 4 papers with code
DropCompute: simple and more robust distributed synchronous training via compute variance reduction
Thus, these methods are limited by the delays caused by straggling workers.
Flood forecasting with machine learning models in an operational framework
During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287, 000 km2, home to more than 350M people.
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions.
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
However, asynchronous training has its pitfalls, mainly a degradation in generalization, even after convergence of the algorithm.
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones.
Augment your batch: better training with larger batches
We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of deep neural networks and datasets.
