no code implementations • CCL 2020 • Chun Chen, Mingyang Li, Fang Kong
中文社交媒体命名实体识别由于其领域特殊性, 一直广受关注。非正式且无结构的微博文本存在以下两个问题:一是词语边界模糊;二是语料规模有限。针对问题一, 本文将同维度的字词进行融合, 获得丰富的文本序列表征;针对问题二, 提出了基于Star-Transformer框架的命名实体识别模型, 借助星型拓扑结构更好地捕获动态特征;同时利用高速网络优化Star-Transformer中的信息桥接, 提升模型的鲁棒性。本文提出的轻量级命名实体识别模型取得了目前Weibo语料上最好的效果。
1 code implementation • 20 Feb 2024 • Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
1 code implementation • 11 Jan 2024 • Wujie Sun, Defang Chen, Jiawei Chen, Yan Feng, Chun Chen, Can Wang
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead.
2 code implementations • 30 Nov 2023 • Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible.
1 code implementation • 13 Aug 2023 • Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
1 code implementation • 26 Jul 2023 • Tongya Zheng, Tianli Zhang, Qingzheng Guan, Wenjie Huang, Zunlei Feng, Mingli Song, Chun Chen
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
1 code implementation • NeurIPS 2023 • Zhiyao Zhou, Sheng Zhou, Bochao Mao, Xuanyi Zhou, Jiawei Chen, Qiaoyu Tan, Daochen Zha, Yan Feng, Chun Chen, Can Wang
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
no code implementations • 9 Jun 2023 • Yang Tian, Zeren Tan, Hedong Hou, Guoqi Li, Aohua Cheng, Yike Qiu, Kangyu Weng, Chun Chen, Pei Sun
These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data.
no code implementations • 31 May 2023 • Defang Chen, Zhenyu Zhou, Jian-Ping Mei, Chunhua Shen, Chun Chen, Can Wang
Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models.
1 code implementation • 15 Apr 2023 • Tongya Zheng, Zunlei Feng, Tianli Zhang, Yunzhi Hao, Mingli Song, Xingen Wang, Xinyu Wang, Ji Zhao, Chun Chen
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
1 code implementation • 15 Apr 2023 • Tongya Zheng, Xinchao Wang, Zunlei Feng, Jie Song, Yunzhi Hao, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen
The whole temporal neighborhood of nodes reveals the varying preferences of nodes.
no code implementations • 28 Dec 2022 • Qihao Shi, Bingyang Fu, Can Wang, Jiawei Chen, Sheng Zhou, Yan Feng, Chun Chen
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
1 code implementation • 22 Nov 2022 • Wujie Sun, Defang Chen, Can Wang, Deshi Ye, Yan Feng, Chun Chen
Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance.
no code implementations • 7 Jun 2022 • Zhehui Zhou, Defang Chen, Can Wang, Yan Feng, Chun Chen
Iteratively incorporating and accumulating iteration-based semantic information enables the low-dimensional model to be more expressive for better link prediction in KGs.
1 code implementation • CVPR 2022 • Defang Chen, Jian-Ping Mei, Hailin Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance.
Ranked #3 on Knowledge Distillation on CIFAR-100
no code implementations • 7 Jan 2022 • Byounghyo Lee, Dongyeon Kim, Seungjae Lee, Chun Chen, Byoungho Lee
Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization.
no code implementations • 28 Dec 2021 • Can Wang, Zhe Wang, Defang Chen, Sheng Zhou, Yan Feng, Chun Chen
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
1 code implementation • 23 Nov 2021 • Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
no code implementations • 14 Sep 2021 • Defang Chen, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a generalized logits matching technique for model compression.
1 code implementation • ACL 2021 • Chun Chen, Fang Kong
In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging.
2 code implementations • 6 Dec 2020 • Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Yan Feng, Chun Chen
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model.
1 code implementation • 16 Nov 2020 • Can Wang, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
no code implementations • 16 Nov 2020 • Jiawei Chen, Chengquan Jiang, Can Wang, Sheng Zhou, Yan Feng, Chun Chen, Martin Ester, Xiangnan He
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
no code implementations • 4 Mar 2020 • Jiawei Chen, Can Wang, Sheng Zhou, Qihao Shi, Jingbang Chen, Yan Feng, Chun Chen
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
2 code implementations • 1 Dec 2019 • Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen
The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i. e., the model used for inference.
no code implementations • 16 Nov 2016 • Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai
In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.
no code implementations • 15 Mar 2016 • Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen
Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation.
no code implementations • CVPR 2014 • Xiao Liu, DaCheng Tao, Mingli Song, Ying Ruan, Chun Chen, Jiajun Bu
In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
no code implementations • CVPR 2014 • Xiao Liu, Mingli Song, DaCheng Tao, Xingchen Zhou, Chun Chen, Jiajun Bu
In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
no code implementations • CVPR 2013 • Xiao Liu, Mingli Song, DaCheng Tao, Zicheng Liu, Luming Zhang, Chun Chen, Jiajun Bu
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.
no code implementations • CVPR 2013 • Luming Zhang, Mingli Song, Zicheng Liu, Xiao Liu, Jiajun Bu, Chun Chen
Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.
no code implementations • 20 May 2012 • Chengxi Ye, Yuxu Lin, Mingli Song, Chun Chen, David W. Jacobs
In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.