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Found 4 papers, 0 papers with code
An End-to-End Time Series Model for Simultaneous Imputation and Forecast
Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values.
Interpretable Clustering via Multi-Polytope Machines
To solve our formulation we propose a two phase approach where we first initialize clusters and polytopes using alternating minimization, and then use coordinate descent to boost clustering performance.
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
These new algorithms can handle statistical and sys- tem heterogeneity, which are the two main challenges in federated learning, while achieving the best known communication complexity.
A Scale Invariant Flatness Measure for Deep Network Minima
It has been empirically observed that the flatness of minima obtained from training deep networks seems to correlate with better generalization.
