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Found 4 papers, 1 papers with code
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data.
Learn Faster and Forget Slower via Fast and Stable Task Adaptation
We empirically show that compared to prevailing fine-tuning practices, FAST learns the target task faster and forgets the source task slower.
Efficient Neural Task Adaptation by Maximum Entropy Initialization
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples.
Learning to Learn with Conditional Class Dependencies
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
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