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Found 6 papers, 2 papers with code
User-LLM: Efficient LLM Contextualization with User Embeddings
Large language models (LLMs) have revolutionized natural language processing.
Mixed Federated Learning: Joint Decentralized and Centralized Learning
For example, additional datacenter data can be leveraged to jointly learn from centralized (datacenter) and decentralized (federated) training data and better match an expected inference data distribution.
What Do We Mean by Generalization in Federated Learning?
Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap).
Learning Federated Representations and Recommendations with Limited Negatives
Deep retrieval models are widely used for learning entity representations and recommendations.
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning.
In-Place Zero-Space Memory Protection for CNN
Convolutional Neural Networks (CNN) are being actively explored for safety-critical applications such as autonomous vehicles and aerospace, where it is essential to ensure the reliability of inference results in the presence of possible memory faults.
