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Found 5 papers, 3 papers with code
MultiResFormer: Transformer with Adaptive Multi-Resolution Modeling for General Time Series Forecasting
Transformer-based models have greatly pushed the boundaries of time series forecasting recently.
DuETT: Dual Event Time Transformer for Electronic Health Records
Electronic health records (EHRs) recorded in hospital settings typically contain a wide range of numeric time series data that is characterized by high sparsity and irregular observations.
A Framework for Neural Network Pruning Using Gibbs Distributions
It can be used for structured or unstructured pruning and we propose a number of specific methods for each.
Regularizing Neural Networks by Stochastically Training Layer Ensembles
Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models.
Survey of Dropout Methods for Deep Neural Networks
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice.
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