no code implementations • CCL 2021 • Ziyi Huang, Junhui Li, ZhengXian Gong
“抽象语义表示(Abstract Meaning Representation, 简称AMR)是将给定的文本的语义特征抽象成一个单根的有向无环图。AMR语义解析则是根据输入的文本获取对应的AMR图。相比于英文AMR, 中文AMR的研究起步较晚, 造成针对中文的AMR语义解析相关研究较少。本文针对公开的中文AMR语料库CAMR1. 0, 采用序列到序列的方法进行中文AMR语义解析的相关研究。具体地, 首先基于Transformer模型实现一个适用于中文的序列到序列AMR语义解析系统;然后, 探索并比较了不同预训练模型在中文AMR语义解析中的应用。基于该语料, 本文中文AMR语义解析方法最优性能达到了70. 29的Smatch F1值。本文是第一次在该数据集上报告实验结果。”
no code implementations • 2 Aug 2023 • Ziyi Huang, Hongshan Liu, Haofeng Zhang, Xueshen Li, Haozhe Liu, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
1 code implementation • LREC 2022 • Jiashu Pu, Ziyi Huang, Yadong Xi, Guandan Chen, WeiJie Chen, Rongsheng Zhang
As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications.
no code implementations • 9 Jun 2022 • Ziyi Huang, Henry Lam, Haofeng Zhang
To overcome these restrictions, we study conditional generative models for aleatoric uncertainty estimation.
no code implementations • 9 Jun 2022 • Ziyi Huang, Yu Gan, Theresa Lye, Yanchen Liu, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon
To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.
no code implementations • 23 Oct 2021 • Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification is at the core of the reliability and robustness of machine learning.
no code implementations • 26 Feb 2021 • Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, Haofeng Zhang
We study the generation of prediction intervals in regression for uncertainty quantification.
no code implementations • 31 Jan 2021 • Ziyi Huang, Haofeng Zhang, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.
no code implementations • 1 Jan 2021 • Ziyi Huang, Henry Lam, Haofeng Zhang
Deep learning has achieved state-of-the-art performance to generate high-quality prediction intervals (PIs) for uncertainty quantification in regression tasks.