Search Results for author:
Found 5 papers, 3 papers with code
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization
Our convergence analysis and experiments prove this is a better way to improve convergence stability and rate in ZO-SGD.
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems
This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques.
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning
To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants.
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data
Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL.
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).
