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Found 4 papers, 1 papers with code
MG-BERT: Multi-Graph Augmented BERT for Masked Language Modeling
Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in Natural Language Processing (NLP) tasks.
More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms
We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory.
PeFLL: Personalized Federated Learning by Learning to Learn
We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones.
Communication-Efficient Federated Learning With Data and Client Heterogeneity
Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally.
