Search Results for author: Ivan Lee

Found 15 papers, 2 papers with code

Explainable Knowledge Distillation for On-device Chest X-Ray Classification

no code implementations10 May 2023 Chakkrit Termritthikun, Ayaz Umer, Suwichaya Suwanwimolkul, Feng Xia, Ivan Lee

To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2

CHIEF: Clustering with Higher-order Motifs in Big Networks

no code implementations6 Apr 2022 Feng Xia, Shuo Yu, Chengfei Liu, Ivan Lee

In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.

Clustering

Web of Scholars: A Scholar Knowledge Graph

no code implementations23 Feb 2022 Jiaying Liu, Jing Ren, Wenqing Zheng, Lianhua Chi, Ivan Lee, Feng Xia

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science.

Heterogeneous Graph Learning for Explainable Recommendation over Academic Networks

no code implementations16 Feb 2022 Xiangtai Chen, Tao Tang, Jing Ren, Ivan Lee, Honglong Chen, Feng Xia

We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation.

Explainable Recommendation Graph Attention +1

Deep Video Anomaly Detection: Opportunities and Challenges

no code implementations11 Oct 2021 Jing Ren, Feng Xia, Yemeng Liu, Ivan Lee

Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.

Anomaly Detection Video Anomaly Detection

EEEA-Net: An Early Exit Evolutionary Neural Architecture Search

1 code implementation13 Aug 2021 Chakkrit Termritthikun, Yeshi Jamtsho, Jirarat Ieamsaard, Paisarn Muneesawang, Ivan Lee

The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold.

Image Classification Keypoint Detection +4

OFFER: A Motif Dimensional Framework for Network Representation Learning

no code implementations27 Aug 2020 Shuo Yu, Feng Xia, Jin Xu, Zhikui Chen, Ivan Lee

In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.

Clustering Graph Learning +2

The Role of Positive and Negative Citations in Scientific Evaluation

no code implementations10 Aug 2020 Xiaomei Bai, Ivan Lee, Zhaolong Ning, Amr Tolba, Feng Xia

Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities.

Scientific Paper Recommendation: A Survey

no code implementations10 Aug 2020 Xiaomei Bai, Mengyang Wang, Ivan Lee, Zhuo Yang, Xiangjie Kong, Feng Xia

The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation.

Collaborative Filtering Recommendation Systems

Multivariate Relations Aggregation Learning in Social Networks

no code implementations9 Aug 2020 Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia

Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.

Attribute Graph Learning +1

DINE: A Framework for Deep Incomplete Network Embedding

no code implementations9 Aug 2020 Ke Hou, Jiaying Liu, Yin Peng, Bo Xu, Ivan Lee, Feng Xia

Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks.

General Classification Link Prediction +3

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition

no code implementations21 Apr 2017 Sebastien C. Wong, Victor Stamatescu, Adam Gatt, David Kearney, Ivan Lee, Mark D. McDonnell

We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types.

General Classification Multi-Object Tracking +4

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