1 code implementation • 27 May 2024 • Xin He, Wenqi Fan, Ruobing Wang, Yili Wang, Ying Wang, Shirui Pan, Xin Wang
More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely.
no code implementations • 16 May 2024 • Huiling Zhou, Xianhao Wu, Hongming Chen, Xiang Chen, Xin He
To this end, we propose the first lightweight network on the mamba-based model called RSDhamba in the field of RSID.
no code implementations • 29 Apr 2024 • Lingbo Huang, Yushi Chen, Xin He
Among the many deep models, Transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI.
1 code implementation • 8 Feb 2024 • Wenhui Chang, Hongming Chen, Xin He, Xiang Chen, Liangduo Shen
Raindrops adhering to the lens of UAVs can obstruct visibility of the background scene and degrade image quality.
no code implementations • 6 Jan 2024 • Xin He, Longhui Wei, Lingxi Xie, Qi Tian
Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months.
no code implementations • 12 Dec 2023 • Jianwei Zhang, Helian Feng, Xin He, Grant P. Strimel, Farhad Ghassemi, Ali Kebarighotbi
We present a novel search optimization solution for approximate nearest neighbor (ANN) search on resource-constrained edge devices.
1 code implementation • 6 Dec 2023 • Xumeng Han, Longhui Wei, Xuehui Yu, Zhiyang Dou, Xin He, Kuiran Wang, Zhenjun Han, Qi Tian
The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting.
no code implementations • 4 Nov 2023 • Rui Yan, Xiaoming Duan, Rui Zou, Xin He, Zongying Shi, Francesco Bullo
We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation.
no code implementations • 31 Oct 2023 • Xin He, Shaoli Huang, Xiaohang Zhan, Chao Weng, Ying Shan
Our framework comprises a Semantic Enhancement module and a Context-Attuned Motion Denoiser (CAMD).
no code implementations • 21 Oct 2023 • Chao Wang, Caixing Wang, Xin He, Xingdong Feng
This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space.
no code implementations • 16 Oct 2023 • Mingyang Ren, Xin He, Junhui Wang
Directed acyclic graph (DAG) has been widely employed to represent directional relationships among a set of collected nodes.
1 code implementation • NeurIPS 2023 • Xingdong Feng, Xin He, Caixing Wang, Chao Wang, Jingnan Zhang
Two types of covariate shift problems are the focus of this paper and the sharp convergence rates are established for a general loss function to provide a unified theoretical analysis, which concurs with the optimal results in literature where the squared loss is used.
2 code implementations • 26 Sep 2023 • Haihao Shen, Naveen Mellempudi, Xin He, Qun Gao, Chang Wang, Mengni Wang
Recent advances in deep learning methods such as LLMs and Diffusion models have created a need for improved quantization methods that can meet the computational demands of these modern architectures while maintaining accuracy.
2 code implementations • 11 Sep 2023 • Wenhua Cheng, Weiwei Zhang, Haihao Shen, Yiyang Cai, Xin He, Kaokao Lv, Yi Liu
Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant challenges due to substantial memory and storage requirements.
no code implementations • 3 Sep 2023 • Zhenheng Tang, Yuxin Wang, Xin He, Longteng Zhang, Xinglin Pan, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Bingsheng He, Xiaowen Chu
The rapid growth of memory and computation requirements of large language models (LLMs) has outpaced the development of hardware, hindering people who lack large-scale high-end GPUs from training or deploying LLMs.
no code implementations • 23 May 2023 • Ruiqi Sun, Siwei Ye, Jie Zhao, Xin He, Yiran Li, An Zou
The inherent diversity of computation types within individual Deep Neural Network (DNN) models imposes a corresponding need for a varied set of computation units within hardware processors.
no code implementations • 7 Apr 2023 • Yalu Wang, Zhijie Han, Jie Li, Xin He
To address the above issue, this paper proposes a graph neural network algorithm based on behavior similarity (BS-GAT) using graph attention network.
no code implementations • 7 Mar 2023 • Jiacheng Li, Longhui Wei, Zongyuan Zhan, Xin He, Siliang Tang, Qi Tian, Yueting Zhuang
To better accelerate the generative transformers while keeping good generation quality, we propose Lformer, a semi-autoregressive text-to-image generation model.
1 code implementation • 23 Nov 2022 • Xin He, Jiangchao Yao, Yuxin Wang, Zhenheng Tang, Ka Chu Cheung, Simon See, Bo Han, Xiaowen Chu
One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i. e., subnet).
1 code implementation • SIGKDD 2022 • Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, Xin Wang
Based on the pre-trained model, we propose the graph prompting function to modify the standalone node into a token pair, and reformulate the downstream node classification looking the same as edge prediction.
1 code implementation • 4 Aug 2022 • Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, Siliang Tang
Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks.
1 code implementation • 6 Jun 2022 • Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu
In federated learning (FL), model performance typically suffers from client drift induced by data heterogeneity, and mainstream works focus on correcting client drift.
1 code implementation • 30 Nov 2021 • Guohao Ying, Xin He, Bin Gao, Bo Han, Xiaowen Chu
Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training.
Ranked #10 on Image Generation on STL-10
no code implementations • 1 Nov 2021 • Ruixuan Zhao, Xin He, Junhui Wang
The proposed method leverages a novel concept of topological layer to facilitate the DAG learning.
no code implementations • 1 Nov 2021 • Wei Zhou, Xin He, Wei Zhong, Junhui Wang
Directed acyclic graph (DAG) models are widely used to represent causal relationships among random variables in many application domains.
no code implementations • 18 Oct 2021 • Shaogao Lv, Xin He, Junhui Wang
This paper considers the partially functional linear model (PFLM) where all predictive features consist of a functional covariate and a high dimensional scalar vector.
no code implementations • 8 Oct 2021 • Ke Zhang, Sihong Chen, Qi Ju, Yong Jiang, Yucong Li, Xin He
The graph network that is established with patches as the nodes can maximize the mutual learning of similar objects.
1 code implementation • 15 Jul 2021 • Ye Yuan, Jun Liu, Dou Jin, Zuogong Yue, Ruijuan Chen, Maolin Wang, Chuan Sun, Lei Xu, Feng Hua, Xin He, Xinlei Yi, Tao Yang, Hai-Tao Zhang, Shaochun Sui, Han Ding
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
no code implementations • NAACL 2021 • Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.
no code implementations • 19 Apr 2021 • Fei Shen, Xin He, Mengwan Wei, Yi Xie
In this report, we introduce the technical details of our submission to the VIPriors object detection challenge.
1 code implementation • 26 Jan 2021 • Xin He, Guohao Ying, Jiyong Zhang, Xiaowen Chu
We propose a new objective, namely potential, which can help exploit promising models to indirectly reduce the number of models involved in weights training, thus alleviating search instability.
2 code implementations • 14 Jan 2021 • Xin He, Shihao Wang, Xiaowen Chu, Shaohuai Shi, Jiangping Tang, Xin Liu, Chenggang Yan, Jiyong Zhang, Guiguang Ding
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
no code implementations • 1 Jan 2021 • Fan Zhou, Yifeng Pan, Shenghua Zhu, Xin He
Directed acyclic graphs (DAGs) are widely used to model the casual relationships among random variables in many disciplines.
no code implementations • IEEE Access ( Volume 8) 2020 • Junping Xu, Xin He
The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism.
no code implementations • 20 Nov 2019 • Xin He, Shihao Wang, Shaohuai Shi, Zhenheng Tang, Yuxin Wang, Zhihao Zhao, Jing Dai, Ronghao Ni, Xiaofeng Zhang, Xiaoming Liu, Zhili Wu, Wu Yu, Xiaowen Chu
Our results show that object detection can help improve the accuracy of some skin disease classes.
2 code implementations • 2 Oct 2019 • Adith Boloor, Karthik Garimella, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang
One such example is autonomous driving, which often relies on deep learning for perception.
no code implementations • 15 Sep 2019 • Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, Xiaowen Chu
Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training.
no code implementations • ICCV 2019 • MingKun Yang, Yushuo Guan, Minghui Liao, Xin He, Kaigui Bian, Song Bai, Cong Yao, Xiang Bai
Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes.
2 code implementations • 2 Aug 2019 • Xin He, Kaiyong Zhao, Xiaowen Chu
Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience.
no code implementations • 12 Mar 2019 • Adith Boloor, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang
Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception.
no code implementations • 3 Jan 2019 • Xin He, Yeheng Ge, Xingdong Feng
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis.
1 code implementation • 10 Nov 2018 • Shangbang Long, Xin He, Cong Yao
As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning.
3 code implementations • ECCV 2018 • Shangbang Long, Jiaqiang Ruan, Wenjie Zhang, Xin He, Wenhao Wu, Cong Yao
Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks.
Ranked #2 on Curved Text Detection on SCUT-CTW1500
no code implementations • 21 May 2018 • Xin He, Liu Ke, Wenyan Lu, Guihai Yan, Xuan Zhang
The intrinsic error tolerance of neural network (NN) makes approximate computing a promising technique to improve the energy efficiency of NN inference.
no code implementations • 26 Feb 2018 • Xin He, Junhui Wang, Shaogao Lv
Variable selection is central to high-dimensional data analysis, and various algorithms have been developed.
no code implementations • 11 Jun 2017 • Yujing Jiang, Xin He, Mei-Ling Ting Lee, Bernard Rosner, Jun Yan
For independent data, they are available in several R packages such as stats and coin.
Computation