Search Results for author: Zuozhu Liu

Found 26 papers, 12 papers with code

TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer

1 code implementation22 Nov 2023 Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng, Joey Tianyi Zhou, Jin Hao, Haochao Ying, Jian Wu, Zuozhu Liu

Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva.

Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models

no code implementations15 Nov 2023 Tingyu Xie, Qi Li, Yan Zhang, Zuozhu Liu, Hongwei Wang

Exploring the application of powerful large language models (LLMs) on the fundamental named entity recognition (NER) task has drawn much attention recently.

named-entity-recognition Named Entity Recognition +2

How Well Do Text Embedding Models Understand Syntax?

1 code implementation14 Nov 2023 Yan Zhang, Zhaopeng Feng, Zhiyang Teng, Zuozhu Liu, Haizhou Li

Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data.

Empirical Study of Zero-Shot NER with ChatGPT

1 code implementation16 Oct 2023 Tingyu Xie, Qi Li, Jian Zhang, Yan Zhang, Zuozhu Liu, Hongwei Wang

Large language models (LLMs) exhibited powerful capability in various natural language processing tasks.

Arithmetic Reasoning named-entity-recognition +3

A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis

no code implementations5 Jul 2023 Jiaxiang Liu, Tianxiang Hu, Yan Zhang, Xiaotang Gai, Yang Feng, Zuozhu Liu

Recent advances in pretrained vision-language models (VLMs) such as CLIP have shown great performance for zero-shot natural image recognition and exhibit benefits in medical applications.

Image Classification Medical Image Classification

On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning

2 code implementations8 Jun 2023 Jianhong Bai, Zuozhu Liu, Hualiang Wang, Jin Hao, Yang Feng, Huanpeng Chu, Haoji Hu

Recent work shows that the long-tailed learning performance could be boosted by sampling extra in-domain (ID) data for self-supervised training, however, large-scale ID data which can rebalance the minority classes are expensive to collect.

Long-tail Learning Representation Learning +1

DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes

1 code implementation15 Feb 2023 Shenghao Hao, Peiyuan Liu, Yibing Zhan, Kaixun Jin, Zuozhu Liu, Mingli Song, Jenq-Neng Hwang, Gaoang Wang

Although cross-view multi-object tracking has received increased attention in recent years, existing datasets still have several issues, including 1) missing real-world scenarios, 2) lacking diverse scenes, 3) owning a limited number of tracks, 4) comprising only static cameras, and 5) lacking standard benchmarks, which hinder the investigation and comparison of cross-view tracking methods.

Multi-Object Tracking object-detection +1

TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer

no code implementations29 Oct 2022 Huimin Xiong, Kunle Li, Kaiyuan Tan, Yang Feng, Joey Tianyi Zhou, Jin Hao, Zuozhu Liu

Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva.

Multi-Task Learning Segmentation

Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning

1 code implementation22 Aug 2022 Hualiang Wang, Siming Fu, Xiaoxuan He, Hangxiang Fang, Zuozhu Liu, Haoji Hu

To our knowledge, this is the first work to measure representation quality of classifiers and features from the perspective of distribution overlap coefficient.

Image Classification Instance Segmentation +1

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

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.

Segmentation

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.

Clustering Self-Supervised Learning +4

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 +2

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