Towards Unbiased Training in Federated Open-world Semi-supervised Learning

no code implementations • 1 May 2023 • Jie Zhang, Xiaosong Ma, Song Guo, Wenchao Xu

Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data.

Open-World Semi-Supervised Learning Transfer Learning

Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks

no code implementations • 18 Apr 2023 • Ping Gong, Yuxin Ma, Cheng Li, Xiaosong Ma, Sam H. Noh

In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud.

Ten Years after ImageNet: A 360° Perspective on AI

no code implementations • 1 Oct 2022 • Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall, Issa Khalil, Xiaosong Ma, Husrev Taha Sencar, Ingmar Weber, Michael Wooldridge, Ting Yu

The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.

Decision Making Fairness +1

Parameterized Knowledge Transfer for Personalized Federated Learning

1 code implementation • NeurIPS 2021 • Jie Zhang, Song Guo, Xiaosong Ma, Haozhao Wang, Wencao Xu, Feijie Wu

To deal with such model constraints, we exploit the potentials of heterogeneous model settings and propose a novel training framework to employ personalized models for different clients.

Personalized Federated Learning Transfer Learning

Towards the standardization of quantum state verification using optimal strategies

no code implementations • 3 Feb 2020 • Xinhe Jiang, Kun Wang, Kaiyi Qian, Zhaozhong Chen, Zhiyu Chen, Liangliang Lu, Lijun Xia, Fangmin Song, Shining Zhu, Xiaosong Ma

We experimentally obtain the scaling parameter of $r=-0. 88\pm$0. 03 and $-0. 78\pm$0. 07 for nonadaptive and adaptive strategies, respectively.

Quantum Physics Optics