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Found 4 papers, 2 papers with code
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices.
A Survey on Contribution Evaluation in Vertical Federated Learning
Vertical Federated Learning (VFL) has emerged as a critical approach in machine learning to address privacy concerns associated with centralized data storage and processing.
Adapting LLMs for Efficient Context Processing through Soft Prompt Compression
By amalgamating soft prompt compression with sophisticated summarization, SoftPromptComp confronts the dual challenges of managing lengthy contexts and ensuring model scalability.
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.
