Education
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning Zhiqin Y ang
Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting
Scaling Law for Time Series Forecasting
Scaling law that rewards large datasets, complex models and enhanced data granularity has been observed in various fields of deep learning.
Revisiting Implicit Differentiation for Learning Problems in Optimal Control
In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system.
GACL: Exemplar-Free Generalized Analytic Continual Learning
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks.
comprehensive benchmark on eight tasks
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering.
Learning From Biased Soft Labels Hua Yuan 1,2, Y u Shi
The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.