Search Results for author: Zuozhu Liu

Found 12 papers, 4 papers with code

Towards Federated Long-Tailed Learning

no code implementations30 Jun 2022 Zihan Chen, Songshang Liu, Hualiang Wang, Howard H. Yang, Tony Q. S. Quek, Zuozhu Liu

Data privacy and class imbalance are the norm rather than the exception in many machine learning tasks.

Federated Learning Long-tailed Learning

AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications

no code implementations11 Mar 2022 Jin Hao, Jiaxiang Liu, Jin Li, Wei Pan, Ruizhe Chen, Huimin Xiong, Kaiwei Sun, Hangzheng Lin, Wanlu Liu, Wanghui Ding, Jianfei Yang, Haoji Hu, Yueling Zhang, Yang Feng, Zeyu Zhao, Huikai Wu, Youyi Zheng, Bing Fang, Zuozhu Liu, Zhihe Zhao

Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental model), and a fusion model to generate 3D fused crown-root-bone structures with high fidelity and accurate occlusal and dentition information.

Federated Stochastic Gradient Descent Begets Self-Induced Momentum

no code implementations17 Feb 2022 Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor

Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data.

Federated Learning

An Unsupervised Sentence Embedding Method by Mutual Information Maximization

1 code implementation EMNLP 2020 Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing

However, SBERT is trained on corpus with high-quality labeled sentence pairs, which limits its application to tasks where labeled data is extremely scarce.

Self-Supervised Learning Semantic Textual Similarity +2

Biologically Plausible Sequence Learning with Spiking Neural Networks

no code implementations25 Nov 2019 Zuozhu Liu, Thiparat Chotibut, Christopher Hillar, Shaowei Lin

Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (MPN), for sequence learning in spiking neural networks.

Scheduling Policies for Federated Learning in Wireless Networks

no code implementations17 Aug 2019 Howard H. Yang, Zuozhu Liu, Tony Q. S. Quek, H. Vincent Poor

Due to limited bandwidth, only a portion of UEs can be scheduled for updates at each iteration.

Information Theory Signal Processing Information Theory

Vprop: Variational Inference using RMSprop

no code implementations4 Dec 2017 Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal

Overall, this paper presents Vprop as a principled, computationally-efficient, and easy-to-implement method for Bayesian deep learning.

Variational Inference

Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks

no code implementations21 Nov 2017 Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin

The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods for training artificial networks.

Biologically-plausible Training

Variational Adaptive-Newton Method for Explorative Learning

no code implementations15 Nov 2017 Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen

We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning.

Active Learning reinforcement-learning +1

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