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Found 4 papers, 3 papers with code
Multi-view self-supervised learning for multivariate variable-channel time series
However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels.
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients.
On the effectiveness of partial variance reduction in federated learning with heterogeneous data
In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers.
Raman Spectrum Matching with Contrastive Representation Learning
We propose a new machine learning technique for Raman spectrum matching, based on contrastive representation learning, that requires no preprocessing and works with as little as a single reference spectrum from each class.
