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Found 5 papers, 2 papers with code
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning
Federated learning is a rapidly growing area of research, holding the promise of privacy-preserving distributed training on edge devices.
Intrinisic Gradient Compression for Federated Learning
Federated learning is a rapidly-growing area of research which enables a large number of clients to jointly train a machine learning model on privately-held data.
Recommending with Recommendations
In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space, and the ideal recommendations for the source and target tasks (which are non-sensitive) are given by unknown linear transformations of the user information.
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking
To streamline this process and accelerate future development, we introduce SubseasonalClimateUSA, a curated dataset for training and benchmarking subseasonal forecasting models in the United States.
Generalization by Recognizing Confusion
A recently-proposed technique called self-adaptive training augments modern neural networks by allowing them to adjust training labels on the fly, to avoid overfitting to samples that may be mislabeled or otherwise non-representative.
