1 code implementation • NAACL 2022 • Pei Chen, Haotian Xu, Cheng Zhang, Ruihong Huang
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
no code implementations • 17 Apr 2024 • Haotian Xu, Zhaorui Zhang, Sheng Di, Benben Liu, Khalid Ayed Alharthi, Jiannong Cao
We propose a full asynchronous training paradigm, called FedFa, which can guarantee model convergence and eliminate the waiting time completely for federated learning by using a few buffered results on the server for parameter updating.
1 code implementation • 2 Apr 2024 • Yuanyuan Lei, Md Messal Monem Miah, Ayesha Qamar, Sai Ramana Reddy, Jonathan Tong, Haotian Xu, Ruihong Huang
This paper initiates a new task to understand moral opinions towards events in news articles.
1 code implementation • 17 Mar 2024 • Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu, Jianping Wu
Many types of NNs (such as Recurrent Neural Network (RNN), and transformers) that are designed to work with sequential data have advantages over tree-based models, because they can take raw network data as input without complex feature computations on the fly.
no code implementations • 9 Mar 2024 • Wangtao Sun, Haotian Xu, Xuanqing Yu, Pei Chen, Shizhu He, Jun Zhao, Kang Liu
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
no code implementations • 10 Feb 2024 • Haotian Xu, Shuai Liu, Ling Shi
In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance.
no code implementations • 13 Oct 2023 • Fengbo Lan, Shengjie Wang, Yunzhe Zhang, Haotian Xu, Oluwatosin Oseni, Yang Gao, Tao Zhang
Achieving human-like dexterous manipulation remains a crucial area of research in robotics.
no code implementations • 1 Sep 2023 • Haotian Xu
Large language models (LLMs) demonstrate impressive language understanding and contextual learning abilities, making them suitable for natural language processing (NLP) tasks and complex mathematical reasoning.
1 code implementation • 19 Jul 2023 • Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, Jingren Zhou
In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria.
no code implementations • 28 Feb 2023 • Haotian Xu, Shengjie Wang, Zhaolei Wang, Yunzhe Zhang, Qing Zhuo, Yang Gao, Tao Zhang
In the early stage, our method loosens the practical constraints of unsafe transitions (adding extra safety budget) with the aid of a new metric we propose.
no code implementations • 2 Jan 2023 • Shengjie Wang, Fengbo Lan, Xiang Zheng, Yuxue Cao, Oluwatosin Oseni, Haotian Xu, Tao Zhang, Yang Gao
In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization.
no code implementations • 26 May 2022 • Faraz Waseem, Sanjit Menon, Haotian Xu, Debashis Mondal
Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregated insights.
2 code implementations • 9 Oct 2021 • Jinghui Si, Xutan Peng, Chen Li, Haotian Xu, JianXin Li
Event Extraction bridges the gap between text and event signals.
no code implementations • ICCV 2017 • Haotian Xu, Ming Dong, Zichun Zhong
Previous approaches on 3D shape segmentation mostly rely on heuristic processing and hand-tuned geometric descriptors.
no code implementations • 13 Dec 2016 • Yiyan Wang, Haotian Xu, Zhijian Ou
State-of-the-art i-vector based speaker verification relies on variants of Probabilistic Linear Discriminant Analysis (PLDA) for discriminant analysis.
no code implementations • 20 Mar 2016 • Haotian Xu, Zhijian Ou
Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables.