Search Results for author: Yiwei Sun

Found 13 papers, 3 papers with code

Labeled Data Generation with Inexact Supervision

no code implementations8 Jun 2021 Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang

We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.

Classification

Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

no code implementations23 Nov 2020 Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar

Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.

Explainable Models General Classification +2

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

no code implementations28 Jun 2020 Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang

Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.

Self-Supervised Learning

An Ontology-Aware Framework for Audio Event Classification

no code implementations27 Jan 2020 Yiwei Sun, Shabnam Ghaffarzadegan

Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information.

Classification General Classification

LMLFM: Longitudinal Multi-Level Factorization Machine

1 code implementation11 Nov 2019 Junjie Liang, Dongkuan Xu, Yiwei Sun, Vasant Honavar

However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.

Variable Selection

Node Injection Attacks on Graphs via Reinforcement Learning

no code implementations14 Sep 2019 Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar

Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.

Node Classification reinforcement-learning

MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

no code implementations20 Aug 2019 Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar

Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)

Link Prediction MULTI-VIEW LEARNING +2

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

1 code implementation20 Aug 2019 Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang

To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.

Node Classification Transfer Learning

Improving Image Captioning by Leveraging Knowledge Graphs

no code implementations25 Jan 2019 Yimin Zhou, Yiwei Sun, Vasant Honavar

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning.

Image Captioning Knowledge Graphs

Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement

no code implementations6 Nov 2018 Yiwei Sun, Ngot Bui, Tsung-Yu Hsieh, Vasant Honavar

Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks.

Network Embedding

Compositional Stochastic Average Gradient for Machine Learning and Related Applications

no code implementations4 Sep 2018 Tsung-Yu Hsieh, Yasser EL-Manzalawy, Yiwei Sun, Vasant Honavar

Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).

Portfolio Optimization

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