Search Results for author: Mi Luo

Found 4 papers, 1 papers with code

MetaFormer is Actually What You Need for Vision

2 code implementations22 Nov 2021 Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance.

Image Classification Semantic Segmentation

No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

no code implementations NeurIPS 2021 Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, Jiashi Feng

Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model.

Classification Federated Learning

MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

no code implementations22 Jan 2020 Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.

Meta-Learning Model Selection +1

Federated Meta-Learning with Fast Convergence and Efficient Communication

no code implementations22 Feb 2018 Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He

Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.

Federated Learning Meta-Learning +1

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