Search Results for author: Xiaoyan Liu

Found 10 papers, 3 papers with code

Testing the Annotation Consistency of Hallidayan Transitivity Processes: A Multi-variable Structural Approach

no code implementations ISA (LREC) 2022 Min Dong, Xiaoyan Liu, Alex Chengyu Fang

SFL seeks to explain identifiable, observable phenomena of language use in context through the application of a theoretical framework which models language as a functional, meaning making system (Halliday & Matthiessen 2004).

Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling

no code implementations21 Mar 2024 Yong He, Hongshan Yu, Muhammad Ibrahim, Xiaoyan Liu, Tongjia Chen, Anwaar Ulhaq, Ajmal Mian

This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification.

Position Segmentation +1

Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction

no code implementations29 Oct 2023 Hao Zhang, Yang Liu, Xiaoyan Liu, Tianming Liang, Gaurav Sharma, Liang Xue, Maozu Guo

We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and important domain of biomedical data.

Relation Relation Extraction +1

Full Point Encoding for Local Feature Aggregation in 3D Point Clouds

no code implementations8 Mar 2023 Yong He, Hongshan Yu, Zhengeng Yang, Xiaoyan Liu, Wei Sun, Ajmal Mian

In particular, we achieve state-of-the-art semantic segmentation results of 76% mIoU on S3DIS 6-fold and 72. 2% on S3DIS Area5.

object-detection Object Detection +2

Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles

no code implementations22 Sep 2022 Xiaoyan Liu, Zehui Dong, Zhiwei Xu, Siyuan Liu, Jie Tian

Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks.

Decision Making Federated Learning

A Discrete-event-based Simulator for Distributed Deep Learning

no code implementations2 Dec 2021 Xiaoyan Liu, Zhiwei Xu, Yana Qin, Jie Tian

New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way.

Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

1 code implementation24 May 2021 Tianming Liang, Yang Liu, Xiaoyan Liu, Hao Zhang, Gaurav Sharma, Maozu Guo

On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously.

Denoising Relation +2

Deep Learning Based 3D Segmentation: A Survey

no code implementations9 Mar 2021 Yong He, Hongshan Yu, Xiaoyan Liu, Zhengeng Yang, Wei Sun, Ajmal Mian

This paper fills the gap and provides a comprehensive survey of the recent progress made in deep learning based 3D segmentation.

Autonomous Driving Point Cloud Segmentation +2

Differentially Private Synthetic Data: Applied Evaluations and Enhancements

1 code implementation11 Nov 2020 Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar, Eduardo de Leon, Anuj Desai, Joshua Allen

Differentially private data synthesis protects personal details from exposure, and allows for the training of differentially private machine learning models on privately generated datasets.

BIG-bench Machine Learning

The Deep Learning Compiler: A Comprehensive Survey

1 code implementation6 Feb 2020 Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Lin Gan, Guangwen Yang, Depei Qian

In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.

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