Search Results for author: Yuan Jin

Found 19 papers, 4 papers with code

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

1 code implementation7 Nov 2022 Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.

Language Modelling Quantization +3

The HoloLens in Medicine: A systematic Review and Taxonomy

no code implementations6 Sep 2022 Christina Gsaxner, Jianning Li, Antonio Pepe, Yuan Jin, Jens Kleesiek, Dieter Schmalstieg, Jan Egger

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.

Leveraging Information Bottleneck for Scientific Document Summarization

no code implementations Findings (EMNLP) 2021 Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan

This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.

Document Summarization Language Modelling +2

Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation

1 code implementation11 Aug 2021 Jianning Li, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jan Egger

However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved.

Image Generation

AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo

no code implementations6 Aug 2021 Yuan Jin, Antonio Pepe, Jianning Li, Christina Gsaxner, Fen-hua Zhao, Jens Kleesiek, Alejandro F. Frangi, Jan Egger

The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if combined with a contrast agent, resulting in a CT angiography (CTA).

Computed Tomography (CT)

Federated Learning Meets Natural Language Processing: A Survey

no code implementations27 Jul 2021 Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang

Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.

Federated Learning

Topic Modelling Meets Deep Neural Networks: A Survey

no code implementations28 Feb 2021 He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine

Topic modelling has been a successful technique for text analysis for almost twenty years.

Navigate Text Generation +1

Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact

no code implementations16 Nov 2020 Jan Egger, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jianning Li, Roman Kern

These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.

object-detection Object Detection

Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning

no code implementations12 Nov 2020 Yuan Jin, Wray Buntine, Francois Petitjean, Geoffrey I. Webb

For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task.

Self-Supervised Learning

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

1 code implementation17 Jul 2020 Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari

Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.

Document Summarization Multi-Document Summarization

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

no code implementations15 Jun 2020 Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin

It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.

Privacy Preserving

Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data

no code implementations12 Oct 2019 Yuan Jin, Ming Liu, Yunfeng Li, Ruohua Xu, Lan Du, Longxiang Gao, Yong Xiang

Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.

A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control

no code implementations5 Dec 2018 Yuan Jin, Mark Carman, Ye Zhu, Yong Xiang

Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.

BIG-bench Machine Learning

Hierarchical clustering that takes advantage of both density-peak and density-connectivity

1 code implementation8 Oct 2018 Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova

This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.

Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing

no code implementations12 Feb 2018 Yuan Jin, Mark Carman, Ye Zhu, Wray Buntine

Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.

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