Search Results for author: Xiping Hu

Found 18 papers, 8 papers with code

History, Development, and Principles of Large Language Models-An Introductory Survey

no code implementations10 Feb 2024 Zhibo Chu, Shiwen Ni, Zichong Wang, Xi Feng, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang, Wenbin Zhang

Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.

Language Modelling

E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models

1 code implementation29 Jan 2024 Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang

The integration of LLMs and education is getting closer and closer, however, there is currently no benchmark for evaluating LLMs that focuses on the Chinese K-12 education domain.

Ethics Multiple-choice

Forgetting before Learning: Utilizing Parametric Arithmetic for Knowledge Updating in Large Language Models

no code implementations14 Nov 2023 Shiwen Ni, Dingwei Chen, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang

In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge.

Expression Syntax Information Bottleneck for Math Word Problems

1 code implementation24 Oct 2023 Jing Xiong, Chengming Li, Min Yang, Xiping Hu, Bin Hu

To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features.


Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

no code implementations31 Mar 2023 Yanjie Dong, Luya Wang, Yuanfang Chi, Jia Wang, Haijun Zhang, Fei Richard Yu, Victor C. M. Leung, Xiping Hu

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels.

Federated Learning

Self-consistent Reasoning For Solving Math Word Problems

no code implementations27 Oct 2022 Jing Xiong, Zhongwei Wan, Xiping Hu, Min Yang, Chengming Li

Specifically, we firstly obtain a sub-network by pruning a roberta2tree model, for the sake to use the gap on output distribution between the original roberta2tree model and the pruned sub-network to expose spurious correlative samples.


SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification

1 code implementation5 Jul 2021 Haocong Rao, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE) framework that comprehensively models human body, component relations, and skeleton dynamics from unlabeled skeleton graphs of various scales to learn an effective skeleton representation for person Re-ID.

Person Re-Identification Relation Network

More than Encoder: Introducing Transformer Decoder to Upsample

no code implementations20 Jun 2021 Yijiang Li, Wentian Cai, Ying Gao, Chengming Li, Xiping Hu

The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation.

Image Segmentation Medical Image Segmentation +3

Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification

1 code implementation6 Jun 2021 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

To fully explore body relations, we construct graphs to model human skeletons from different levels, and for the first time propose a Multi-level Graph encoding approach with Structural-Collaborative Relation learning (MG-SCR) to encode discriminative graph features for person Re-ID.

Person Re-Identification Relation

Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition

1 code implementation14 Nov 2020 Shihao Xu, Haocong Rao, Xiping Hu, Bin Hu

Existing approaches usually learn action representations by sequential prediction but they suffer from the inability to fully learn semantic information.

Action Recognition Clustering +5

A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification

1 code implementation5 Sep 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Yi Guo, Jun Cheng, Xinwang Liu, Bin Hu

This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.

Contrastive Learning Person Re-Identification +2

Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification

1 code implementation21 Aug 2020 Haocong Rao, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, Bin Hu

Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner.

Person Re-Identification

Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition

2 code implementations1 Aug 2020 Haocong Rao, Shihao Xu, Xiping Hu, Jun Cheng, Bin Hu

In this paper, we for the first time propose a contrastive action learning paradigm named AS-CAL that can leverage different augmentations of unlabeled skeleton data to learn action representations in an unsupervised manner.

Action Recognition Contrastive Learning

Emotion Recognition From Gait Analyses: Current Research and Future Directions

no code implementations13 Mar 2020 Shihao Xu, Jing Fang, Xiping Hu, Edith Ngai, Wei Wang, Yi Guo, Victor C. M. Leung

This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns.

Emotion Recognition

Towards Interpreting Deep Neural Networks via Understanding Layer Behaviors

no code implementations25 Sep 2019 JieZhang Cao, Jincheng Li, Xiping Hu, Peilin Zhao, Mingkui Tan

ii) the $W$-distance of a specific layer to the target distribution tends to decrease along training iterations.

Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks

no code implementations22 Apr 2018 Fusheng Hao, Jun Cheng, Lei Wang, Xinchao Wang, Jianzhong Cao, Xiping Hu, Dapeng Tao

Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.

Image Classification Test

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