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Found 4 papers, 1 papers with code
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding.
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective
Federated Learning (FL) is a promising paradigm that offers significant advancements in privacy-preserving, decentralized machine learning by enabling collaborative training of models across distributed devices without centralizing data.
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains.
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts
By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models.
