Search Results for author: Jie Yin

Found 29 papers, 7 papers with code

Long-Tail Class Incremental Learning via Independent Sub-prototype Construction

no code implementations CVPR 2024 Xi Wang, Xu Yang, Jie Yin, Kun Wei, Cheng Deng

In this paper we constructed two parallel spaces simultaneously: 1) Sub-prototype space and 2) Reminiscence space to learn robust representations while alleviating forgetfulness.

Class Incremental Learning Incremental Learning +1

Implicit Event-RGBD Neural SLAM

no code implementations CVPR 2024 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.

Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment

no code implementations5 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.

Entity Alignment Knowledge Graphs +1

Conflict-Aware Pseudo Labeling via Optimal Transport for Entity Alignment

1 code implementation5 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.

Entity Alignment Entity Embeddings +2

Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

no code implementations2 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.

Relational Reasoning

LADDER: Latent Boundary-guided Adversarial Training

1 code implementation8 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.

Adversarial Robustness

Link Prediction with Contextualized Self-Supervision

no code implementations25 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.

Attribute Inductive Link Prediction +1

Informative Pseudo-Labeling for Graph Neural Networks with Few Labels

no code implementations20 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.

Informativeness Node Classification +1

Quasi-Framelets: Another Improvement to GraphNeural Networks

no code implementations11 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.

Graph Learning Graph Neural Network

M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots

2 code implementations19 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.

A long-lasting guided bone regeneration membrane from sequentially functionalised photoactive atelocollagen

no code implementations15 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.

Edge but not Least: Cross-View Graph Pooling

no code implementations24 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.

Graph Classification Graph Regression +1

Human-Understandable Decision Making for Visual Recognition

no code implementations5 Mar 2021 Xiaowei Zhou, Jie Yin, Ivor Tsang, Chen Wang

The widespread use of deep neural networks has achieved substantial success in many tasks.

Decision Making

Unified Robust Training for Graph NeuralNetworks against Label Noise

no code implementations5 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.

Learning with noisy labels Node Classification

Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective

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.

SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs

no code implementations22 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.

Active Learning Graph Embedding +1

Latent Adversarial Defence with Boundary-guided Generation

no code implementations16 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.

Search Efficient Binary Network Embedding

1 code implementation14 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.

Attribute Network Embedding +2

Attributed Network Embedding via Subspace Discovery

1 code implementation14 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.

Attribute Clustering +4

SINE: Scalable Incomplete Network Embedding

2 code implementations16 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

MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding

no code implementations7 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

Network Representation Learning: A Survey

no code implementations4 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.

Representation Learning

Interpreting Shared Deep Learning Models via Explicable Boundary Trees

no code implementations12 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.

Decision Making

Transfer Learning across Networks for Collective Classification

no code implementations11 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.

Classification General Classification +1

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