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Found 5 papers, 3 papers with code
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities.
CoSDA: Continual Source-Free Domain Adaptation
Recently, SFDA has gained popularity due to the need to protect the data privacy of the source domain, but it suffers from catastrophic forgetting on the source domain due to the lack of data.
Does Federated Learning Really Need Backpropagation?
BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server.
Good Semi-supervised VAE Requires Tighter Evidence Lower Bound
(2) Good semi-supervised learning results and good generative performance can not be obtained at the same time.
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.
