1 code implementation • 28 May 2024 • Zhengyang Tang, Chenyu Huang, Xin Zheng, Shixi Hu, Zizhuo Wang, Dongdong Ge, Benyou Wang
We apply the data from OR-Instruct to various open-source LLMs of 7b size (termed as ORLMs), resulting in a significantly improved capability for optimization modeling.
1 code implementation • 15 Mar 2024 • Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan
This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.
no code implementations • 3 Mar 2024 • Haiquan Zhao, Xuwu Wang, Shisong Chen, Zhixu Li, Xin Zheng, Yanghua Xiao
In this paper, we propose a task called Online Video Entity Linking OVEL, aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness.
no code implementations • 26 Feb 2024 • Man Wu, Xin Zheng, Qin Zhang, Xiao Shen, Xiong Luo, Xingquan Zhu, Shirui Pan
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data.
1 code implementation • 23 Feb 2024 • Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language.
1 code implementation • 14 Feb 2024 • Xin Zheng, Jianke Zhu
Nowadays, sensor suits have been equipped with redundant LiDARs and IMUs to mitigate the risks associated with sensor failure.
no code implementations • 20 Nov 2023 • Xin Zheng, Ziang Peng, Yuan Cao, Hongming Shan, Junping Zhang
Video prediction, predicting future frames from the previous ones, has broad applications such as autonomous driving and weather forecasting.
no code implementations • 30 Sep 2023 • Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, Jiajun Bu, Philip S. Yu
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
1 code implementation • 25 Sep 2023 • Xin Zheng, Jianke Zhu
Therefore, our proposed Traj-LO approach tries to recover the spatial-temporal consistent movement of LiDAR by tightly coupling the geometric information from LiDAR points and kinematic constraints from trajectory smoothness.
1 code implementation • 20 Sep 2023 • Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, Shirui Pan
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.
1 code implementation • 19 Sep 2023 • Xin Zheng, Hongyu Lin, Xianpei Han, Le Sun
Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types.
no code implementations • 17 Jun 2023 • Jiaan Wang, Jianfeng Qu, Yunlong Liang, Zhixu Li, An Liu, Guanfeng Liu, Xin Zheng
Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence.
1 code implementation • NeurIPS 2023 • Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan
Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.
no code implementations • 24 May 2023 • Zefan Cai, Xin Zheng, Tianyu Liu, Xu Wang, Haoran Meng, Jiaqi Han, Gang Yuan, Binghuai Lin, Baobao Chang, Yunbo Cao
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last updates.
no code implementations • 23 Feb 2023 • Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
no code implementations • 14 Dec 2022 • Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Zhifang Sui, Yunbo Cao
Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios.
1 code implementation • 8 Nov 2022 • Wenhao Zhu, ShuJian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.
no code implementations • 28 Oct 2022 • Peipei Liu, Xin Zheng, Hong Li, Jie Liu, Yimo Ren, Hongsong Zhu, Limin Sun
At the second stage, a self-supervised contrastive learning is designed for the improvement of the distilled unimodal representations after cross-modal interaction.
1 code implementation • 17 Jun 2022 • Xin Zheng, Jianke Zhu
Prism-based LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in robotics, recently.
no code implementations • 14 Feb 2022 • Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S. Yu, Shirui Pan
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
no code implementations • 20 Sep 2021 • Xin Zheng, Yanbo Fan, Baoyuan Wu, Yong Zhang, Jue Wang, Shirui Pan
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
1 code implementation • Findings (EMNLP) 2021 • Xin Zheng, Zhirui Zhang, ShuJian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen
Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
3 code implementations • ACL 2021 • Xin Zheng, Zhirui Zhang, Junliang Guo, ShuJian Huang, Boxing Chen, Weihua Luo, Jiajun Chen
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
no code implementations • 22 Apr 2021 • Xin Zheng, Jianke Zhu
LiDAR odometry plays an important role in self-localization and mapping for autonomous navigation, which is usually treated as a scan registration problem.
1 code implementation • 17 Feb 2021 • Tianyu Liu, Xin Zheng, Baobao Chang, Zhifang Sui
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record.
no code implementations • 15 Feb 2021 • Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H. -S. Philip Wong, Armin Alaghi
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
no code implementations • NeurIPS Workshop DL-IG 2020 • Berivan Isik, Kristy Choi, Xin Zheng, H.-S. Philip Wong, Stefano Ermon, Tsachy Weissman, Armin Alaghi
Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.
1 code implementation • CONLL 2020 • Tianyu Liu, Xin Zheng, Xiaoan Ding, Baobao Chang, Zhifang Sui
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust.
no code implementations • LREC 2020 • Tianyu Liu, Xin Zheng, Baobao Chang, Zhifang Sui
Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise.
no code implementations • 16 Feb 2020 • Jiali Xu, Qian Yin, Ping Guo, Xin Zheng
At the same time, the spectrum extraction results before and after calibration are compared, results show the characteristics of the extracted one-dimensional waveform are more close to an ideal optics system, and the PSF of the corrected object spectrum image estimated by the blind deconvolution method is nearly central symmetry, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy.
no code implementations • IJCNLP 2019 • Xin Zheng, Aixin Sun, Jing Li, Karthik Muthuswamy
In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper.
no code implementations • 26 Dec 2018 • Xin Zheng, Yanqing Guo, Huaibo Huang, Yi Li, Ran He
Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes.
no code implementations • 29 May 2017 • Zhenzhou Wu, Xin Zheng, Daniel Dahlmeier
Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets.
no code implementations • 9 May 2017 • Xin Zheng, Jialong Han, Aixin Sun
Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations.
no code implementations • 14 Feb 2015 • Soheil Keshmiri, Xin Zheng, Chee Meng Chew, Chee Khiang Pang
We present a deep learning approach to estimation of the bead parameters in welding tasks.
no code implementations • 23 Sep 2013 • Xin Zheng, Zhiyong Wu, Helen Meng, Weifeng Li, Lianhong Cai
In this paper, we first present a new variant of Gaussian restricted Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann machine (MGRBM), with its definition and learning algorithm.