no code implementations • 7 Apr 2024 • Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, Xi Xiao
Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content.
1 code implementation • 21 Mar 2024 • Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
no code implementations • 18 Mar 2024 • Yuxin Cao, Jinghao Li, Xi Xiao, Derui Wang, Minhui Xue, Hao Ge, Wei Liu, Guangwu Hu
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
1 code implementation • 26 Feb 2024 • Hao Wang, Zeyu Gao, Chao Zhang, Zihan Sha, Mingyang Sun, Yuchen Zhou, Wenyu Zhu, Wenju Sun, Han Qiu, Xi Xiao
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
1 code implementation • 12 Feb 2024 • Haozhen Zhang, Xi Xiao, Le Yu, Qing Li, Zhen Ling, Ye Zhang
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
1 code implementation • 15 Dec 2023 • Yuxin Cao, Ziyu Zhao, Xi Xiao, Derui Wang, Minhui Xue, Jin Lu
We separate the attack into three stages: style reference selection, reinforcement-learning-based logo style transfer, and perturbation optimization.
1 code implementation • 13 Dec 2023 • Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu
To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE).
no code implementations • 30 Nov 2023 • Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.
1 code implementation • 16 Aug 2023 • Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.
1 code implementation • 31 Jul 2023 • Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
1 code implementation • 13 Jun 2023 • Haozhen Zhang, Xueting Han, Xi Xiao, Jing Bai
To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges.
no code implementations • 30 Oct 2022 • Fuyang Li, Jiying Zhang, Xi Xiao, Bin Zhang, Dijun Luo
This paper proposes a two-phase paradigm to aggregate comprehensive information on discrete structures leading to a Discount Markov Diffusion Learnable Kernel (DMDLK).
no code implementations • 19 Aug 2022 • Guanzi Chen, Jiying Zhang, Xi Xiao, Yang Li
In this paper, we present a novel test time adaptation strategy named Graph Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to better adapt to the Out Of Distribution (OOD) test data.
no code implementations • 31 May 2022 • Hailong Ma, Sibo Feng, Xi Xiao, Chenyu Dong, Xingyue Cheng
Photo retouching aims to adjust the luminance, contrast, and saturation of the image to make it more human aesthetically desirable.
5 code implementations • 19 May 2022 • Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
2 code implementations • 31 Mar 2022 • Jiying Zhang, Fuyang Li, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, Yatao Bian
As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.
no code implementations • 31 Mar 2022 • Guanzi Chen, Jiying Zhang, Xi Xiao, Yang Li
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships.
1 code implementation • 30 Mar 2022 • Yuxin Cao, Xi Xiao, Ruoxi Sun, Derui Wang, Minhui Xue, Sheng Wen
In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system.
no code implementations • 23 Mar 2022 • Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
1 code implementation • 20 Mar 2022 • Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, Yatao Bian
In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones.
Ranked #1 on Graph Classification on BBBP
no code implementations • 17 Feb 2022 • Yuhan Yao, Yuhe Zhao, Yanxian Wei, Feng Zhou, Daigao Chen, Yuguang Zhang, Xi Xiao, Ming Li, Jianji Dong, Shaohua Yu, Xinliang Zhang
We demonstrate a fully-integrated multipurpose microwave frequency identification system on silicon-on-insulator platform.
no code implementations • 24 Jan 2022 • Mingzhe Chen, Xi Xiao, Bin Zhang, Xinyu Liu, Runiu Lu
In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset.
2 code implementations • 28 Oct 2021 • Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
Ranked #3 on Recommendation Systems on Gowalla
no code implementations • 29 Sep 2021 • Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng
Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.
1 code implementation • 26 Sep 2021 • Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #3 on Recommendation Systems on Yelp2018
no code implementations • 26 Aug 2021 • Wanpeng Zhang, Xiaoyan Cao, Yao Yao, Zhicheng An, Xi Xiao, Dijun Luo
In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges.
no code implementations • 3 Aug 2021 • Wanpeng Zhang, Xi Xiao, Yao Yao, Mingzhe Chen, Dijun Luo
MBDP consists of two kinds of dropout mechanisms, where the rollout-dropout aims to improve the robustness with a small cost of sample efficiency, while the model-dropout is designed to compensate for the lost efficiency at a slight expense of robustness.
1 code implementation • 12 Jun 2021 • Jiying Zhang, Yuzhao Chen, Xi Xiao, Runiu Lu, Shu-Tao Xia
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data.
1 code implementation • 10 Jun 2021 • Jiying Zhang, Yuzhao Chen, Xi Xiao, Runiu Lu, Shu-Tao Xia
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data.
no code implementations • 17 Mar 2021 • Yuzhao Chen, Yatao Bian, Jiying Zhang, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.
no code implementations • 28 Dec 2020 • Bowen Zhao, Chen Chen, Xi Xiao, Shutao Xia
Object detectors are typically learned on fully-annotated training data with fixed predefined categories.
1 code implementation • NeurIPS 2020 • Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying WEI, Junzhou Huang
Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.
Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +1
no code implementations • 4 Nov 2020 • Yuzhao Chen, Yatao Bian, Xi Xiao, Yu Rong, Tingyang Xu, Junzhou Huang
Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.
no code implementations • 26 Aug 2020 • Kelong Mao, Xi Xiao, Jieming Zhu, Biao Lu, Ruiming Tang, Xiuqiang He
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
no code implementations • 12 Jul 2020 • Tian Bian, Xi Xiao, Tingyang Xu, Yu Rong, Wenbing Huang, Peilin Zhao, Junzhou Huang
Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.
2 code implementations • 17 Jan 2020 • Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, Junzhou Huang
Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.
1 code implementation • CVPR 2020 • Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, Shu-Tao Xia
In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes.
Ranked #2 on Incremental Learning on ImageNet100 - 10 steps (# M Params metric)
no code implementations • 25 Sep 2019 • Kelong Mao, Peilin Zhao, Tingyang Xu, Yu Rong, Xi Xiao, Junzhou Huang
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers.
Ranked #17 on Single-step retrosynthesis on USPTO-50k
no code implementations • 13 Apr 2019 • Bowen Zhao, Xi Xiao, Wanpeng Zhang, Bin Zhang, Shu-Tao Xia
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).