no code implementations • 18 Nov 2023 • Delin Qu, Chi Yan, Dong Wang, Jie Yin, Dan Xu, Bin Zhao, Xuelong Li
To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping.
no code implementations • 5 Jul 2023 • Qijie Ding, Jie Yin, Daokun Zhang, Junbin Gao
Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity.
1 code implementation • 5 Sep 2022 • Qijie Ding, Daokun Zhang, Jie Yin
The key idea is to iteratively pseudo-label alignment pairs empowered with conflict-aware optimal transport (OT) modeling to boost the precision of entity alignment.
no code implementations • 2 Sep 2022 • Han Wu, Jie Yin, Bala Rajaratnam, Jianyuan Guo
By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations.
1 code implementation • 8 Jun 2022 • Xiaowei Zhou, Ivor W. Tsang, Jie Yin
To achieve a better trade-off between standard accuracy and adversarial robustness, we propose a novel adversarial training framework called LAtent bounDary-guided aDvErsarial tRaining (LADDER) that adversarially trains DNN models on latent boundary-guided adversarial examples.
no code implementations • 25 Jan 2022 • Daokun Zhang, Jie Yin, Philip S. Yu
To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance.
no code implementations • 20 Jan 2022 • Yayong Li, Jie Yin, Ling Chen
It aims to augment the training set with pseudo-labeled unlabeled nodes with high confidence so as to re-train a supervised model in a self-training cycle.
no code implementations • 11 Jan 2022 • Mengxi Yang, Xuebin Zheng, Jie Yin, Junbin Gao
This paper aims to provide a novel design of a multiscale framelets convolution for spectral graph neural networks.
2 code implementations • 19 Dec 2021 • Jie Yin, Ang Li, Tao Li, Wenxian Yu, Danping Zou
We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals.
no code implementations • 15 Dec 2021 • He Liang, Jie Yin, Kenny Man, Xuebin B. Yang, Elena Calciolari, Nikolaos Donos, Stephen J. Russell, David J. Wood, Giuseppe Tronci
The fast degradation of collagen-based membranes in the biological environment remains a critical challenge, resulting in underperforming Guided Bone Regeneration (GBR) therapy leading to compromised clinical results.
no code implementations • 24 Sep 2021 • Xiaowei Zhou, Jie Yin, Ivor W. Tsang
Graph neural networks have emerged as a powerful model for graph representation learning to undertake graph-level prediction tasks.
1 code implementation • 9 Sep 2021 • Zhi Wang, Chaoge Liu, Xiang Cui, Jie Yin, Xutong Wang
Therefore, we propose an improved stegomalware EvilModel.
no code implementations • 5 Mar 2021 • Xiaowei Zhou, Jie Yin, Ivor Tsang, Chen Wang
The widespread use of deep neural networks has achieved substantial success in many tasks.
no code implementations • 5 Mar 2021 • Yayong Li, Jie Yin, Ling Chen
Learning with label noise has been primarily studied in the context of image classification, but these techniques cannot be directly applied to graph-structured data, due to two major challenges -- label sparsity and label dependency -- faced by learning on graphs.
no code implementations • ICLR 2022 • Wei Huang, Yayong Li, Weitao Du, Jie Yin, Richard Yi Da Xu, Ling Chen, Miao Zhang
Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally.
no code implementations • 16 Sep 2020 • Zhi Wang, Chaoge Liu, Xiang Cui, Jie Yin, Jiaxi Liu, Di wu, Qixu Liu
The defender can limit the attacker once it is exposed.
no code implementations • 22 Aug 2019 • Yayong Li, Jie Yin, Ling Chen
In this paper, we propose a SEmi-supervised Adversarial active Learning (SEAL) framework on attributed graphs, which fully leverages the representation power of deep neural networks and devises a novel AL query strategy in an adversarial way.
no code implementations • 16 Jul 2019 • Xiaowei Zhou, Ivor W. Tsang, Jie Yin
The proposed LAD method improves the robustness of a DNN model through adversarial training on generated adversarial examples.
1 code implementation • 14 Jan 2019 • Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network.
1 code implementation • 14 Jan 2019 • Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
Ranked #1 on Node Clustering on Facebook
2 code implementations • 16 Oct 2018 • Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs.
Social and Information Networks
no code implementations • 7 Mar 2018 • Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes.
Social and Information Networks
no code implementations • 4 Dec 2017 • Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information.
no code implementations • 12 Sep 2017 • Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu
In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model.
no code implementations • 11 Mar 2014 • Meng Fang, Jie Yin, Xingquan Zhu
In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks.