Search Results for author: Yizhou Sun

Found 64 papers, 21 papers with code

Differentiable Product Quantization for Learning Compact Embedding Layers

no code implementations ICML 2020 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.

Quantization

Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics

no code implementations11 Jan 2022 Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao

To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.

Model Selection

Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks

no code implementations17 Nov 2021 Atefeh Sohrabizadeh, Yunsheng Bai, Yizhou Sun, Jason Cong

High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level.

Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation

no code implementations17 Oct 2021 Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah

We show that GLNN with competitive performance infer faster than GNNs by 146X-273X and faster than other acceleration methods by 14X-27X.

Knowledge Distillation Node Classification +2

Deep Learning of Potential Outcomes

1 code implementation9 Oct 2021 Bernard Koch, Tim Sainburg, Pablo Geraldo, Song Jiang, Yizhou Sun, Jacob Gates Foster

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework.

Causal Inference

Towards Fine-Grained Reasoning for Fake News Detection

no code implementations13 Sep 2021 Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.

Fake News Detection

Fuzzy Logic based Logical Query Answering on Knowledge Graph

no code implementations5 Aug 2021 Xuelu Chen, Ziniu Hu, Yizhou Sun

Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task.

Knowledge Graphs Link Prediction

Clinical Named Entity Recognition using Contextualized Token Representations

no code implementations23 Jun 2021 Yichao Zhou, Chelsea Ju, J. Harry Caufield, Kevin Shih, Calvin Chen, Yizhou Sun, Kai-Wei Chang, Peipei Ping, Wei Wang

To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.

Language Modelling Named Entity Recognition +1

Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts

1 code implementation15 Mar 2021 Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, Wei Wang

The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities.

Entity Typing Knowledge Graphs +1

Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases

1 code implementation7 Mar 2021 Junheng Hao, Chelsea Ju, Muhao Chen, Yizhou Sun, Carlo Zaniolo, Wei Wang

Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species.

Representation Learning Type prediction

CREATe: Clinical Report Extraction and Annotation Technology

no code implementations28 Feb 2021 Yichao Zhou, Wei-Ting Chen, BoWen Zhang, David Lee, J. Harry Caufield, Kai-Wei Chang, Yizhou Sun, Peipei Ping, Wei Wang

Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.

Decoupled Greedy Learning of Graph Neural Networks

no code implementations1 Jan 2021 Yewen Wang, Jian Tang, Yizhou Sun, Guy Wolf

We empirically analyse our proposed DGL-GNN model, and demonstrate its effectiveness and superior efficiency through a range of experiments.

Learning to Search for Fast Maximum Common Subgraph Detection

no code implementations1 Jan 2021 Yunsheng Bai, Derek Qiang Xu, Yizhou Sun, Wei Wang

Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc.

Graph Matching Imitation Learning +1

Motif-Driven Contrastive Learning of Graph Representations

no code implementations23 Dec 2020 Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun

Our framework MotIf-driven Contrastive leaRning Of Graph representations (MICRO-Graph) can: 1) use GNNs to extract motifs from large graph datasets; 2) leverage learned motifs to sample informative subgraphs for contrastive learning of GNN.

Contrastive Learning Self-Supervised Learning

Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks

no code implementations18 Dec 2020 Xiang Li, Danhao Ding, Ben Kao, Yizhou Sun, Nikos Mamoulis

A heterogeneous information network (HIN) has as vertices objects of different types and as edges the relations between objects, which are also of various types.

General Classification Multi-Task Learning +1

Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs

no code implementations12 Dec 2020 Jiarong Xu, Yizhou Sun, Xin Jiang, Yanhao Wang, Yang Yang, Chunping Wang, Jiangang Lu

To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.

Adversarial Attack Graph Classification +1

Unsupervised Adversarially-Robust Representation Learning on Graphs

no code implementations4 Dec 2020 Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, Yizhou Sun

Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.

Adversarial Robustness Community Detection +4

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

1 code implementation NeurIPS 2020 Zijie Huang, Yizhou Sun, Wei Wang

In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time.

Multivariate Time Series Classification with Hierarchical Variational Graph Pooling

no code implementations12 Oct 2020 Ziheng Duan, Haoyan Xu, Yueyang Wang, Yida Huang, Anni Ren, Zhongbin Xu, Yizhou Sun, Wei Wang

Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.

General Classification Graph Classification +4

Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer

1 code implementation Findings of the Association for Computational Linguistics 2020 Xuelu Chen, Muhao Chen, Changjun Fan, Ankith Uppunda, Yizhou Sun, Carlo Zaniolo

Predicting missing facts in a knowledge graph (KG) is a crucial task in knowledge base construction and reasoning, and it has been the subject of much research in recent works using KG embeddings.

Knowledge Graph Completion Transfer Learning

GPT-GNN: Generative Pre-Training of Graph Neural Networks

1 code implementation27 Jun 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun

Graph neural networks (GNNs) have been demonstrated to be powerful in modeling graph-structured data.

Graph Generation

Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction

no code implementations11 Jun 2020 Yunsheng Bai, Ken Gu, Yizhou Sun, Wei Wang

We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI).

Link Prediction

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding

1 code implementation2 Jun 2020 Zhiping Xiao, Weiping Song, Haoyan Xu, Zhicheng Ren, Yizhou Sun

However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.

CoSimGNN: Towards Large-scale Graph Similarity Computation

no code implementations14 May 2020 Haoyan Xu, Runjian Chen, Yunsheng Bai, Ziheng Duan, Jie Feng, Yizhou Sun, Wei Wang

The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications, such as 3D action recognition and biological molecular identification.

3D Action Recognition Graph Similarity

Software Language Comprehension using a Program-Derived Semantics Graph

no code implementations NeurIPS Workshop CAP 2020 Roshni G. Iyer, Yizhou Sun, Wei Wang, Justin Gottschlich

To continue to advance this research, we present the program-derived semantics graph, a new graphical structure to capture semantics of code.

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark

1 code implementation1 Apr 2020 Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, Jiawei Han

Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.

Network Embedding

Heterogeneous Graph Transformer

3 code implementations3 Mar 2020 Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun

Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data.

Graph Sampling

GLSearch: Maximum Common Subgraph Detection via Learning to Search

no code implementations8 Feb 2020 Yunsheng Bai, Derek Xu, Yizhou Sun, Wei Wang

However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on heuristic search algorithms which in practice cannot find good solution for large graph pairs given a limited computation budget.

Graph Embedding Graph Matching +3

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

1 code implementation NeurIPS 2019 Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu

Original full-batch GCN training requires calculating the representation of all the nodes in the graph per GCN layer, which brings in high computation and memory costs.

Node Classification

Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks

no code implementations25 Sep 2019 Xin Jiang*, Kewei Cheng*, Song Jiang*, Yizhou Sun

Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging.

Node Classification

NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN

no code implementations25 Sep 2019 Kewei Cheng, Yikai Zhu, Ming Zhang, Yizhou Sun

Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks.

Knowledge Graph Completion Knowledge Graph Embedding

Learning to Transfer via Modelling Multi-level Task Dependency

no code implementations25 Sep 2019 Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun

Besides, the understanding of relationships among tasks has been ignored by most of the current methods.

Multi-Task Learning

Learning Compact Embedding Layers via Differentiable Product Quantization

no code implementations25 Sep 2019 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.

Quantization

Demystifying Graph Neural Network Via Graph Filter Assessment

no code implementations25 Sep 2019 Yewen Wang, Ziniu Hu, Yusong Ye, Yizhou Sun

However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.

Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction

no code implementations25 Sep 2019 Yunsheng Bai, Derek Xu, Ken Gu, Xueqing Wu, Agustin Marinovic, Christopher Ro, Yizhou Sun, Wei Wang

Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair.

Differentiable Product Quantization for End-to-End Embedding Compression

1 code implementation26 Aug 2019 Ting Chen, Lala Li, Yizhou Sun

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.

Quantization

Few-Shot Representation Learning for Out-Of-Vocabulary Words

1 code implementation ACL 2019 Ziniu Hu, Ting Chen, Kai-Wei Chang, Yizhou Sun

Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

Learning Word Embeddings Meta-Learning

Pre-Training Graph Neural Networks for Generic Structural Feature Extraction

no code implementations31 May 2019 Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun

With the proposed pre-training procedure, the generic structural information is learned and preserved, thus the pre-trained GNN requires less amount of labeled data and fewer domain-specific features to achieve high performance on different downstream tasks.

Denoising

Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach

1 code implementation24 May 2019 Changjun Fan, Li Zeng, Yuhui Ding, Muhao Chen, Yizhou Sun, Zhong Liu

By training on small-scale networks, the learned model is capable of assigning relative BC scores to nodes for any unseen networks, and thus identifying the highly-ranked nodes.

Community Detection

Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification

1 code implementation11 May 2019 Ting Chen, Song Bian, Yizhou Sun

In this work, we propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.

General Classification Graph Classification

Learning Fair Representations via an Adversarial Framework

1 code implementation30 Apr 2019 Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, Chunping Wang

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval.

Fairness General Classification

Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

1 code implementation1 Apr 2019 Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, Wei Wang

We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity.

General Classification Graph Classification +2

Embedding Uncertain Knowledge Graphs

1 code implementation26 Nov 2018 Xuelu Chen, Muhao Chen, Weijia Shi, Yizhou Sun, Carlo Zaniolo

However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge.

General Classification Knowledge Graphs +1

Convolutional Set Matching for Graph Similarity

no code implementations23 Oct 2018 Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.

Graph Similarity set matching

Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

no code implementations10 Sep 2018 Yunsheng Bai, Hao Ding, Yizhou Sun, Wei Wang

Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.

Combinatorial Optimization Graph Classification +4

The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games

no code implementations26 Jun 2018 Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu, Chris Amato, Seth Cooper, Yizhou Sun, Magy Seif El-Nasr

The selection of heroes, also known as pick or draft, takes place before the match starts and alternates between the two teams until each player has selected one hero.

Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations

no code implementations ICML 2018 Ting Chen, Martin Renqiang Min, Yizhou Sun

Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encoding of the discrete symbols.

HeteroMed: Heterogeneous Information Network for Medical Diagnosis

no code implementations22 Apr 2018 Anahita Hosseini, Ting Chen, Wenjun Wu, Yizhou Sun, Majid Sarrafzadeh

To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis.

Medical Diagnosis

Learning K-way D-dimensional Discrete Code For Compact Embedding Representations

no code implementations8 Nov 2017 Ting Chen, Martin Renqiang Min, Yizhou Sun

Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols.

Language Modelling

On Sampling Strategies for Neural Network-based Collaborative Filtering

no code implementations23 Jun 2017 Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong

In this paper, we propose a general neural network-based recommendation framework, which subsumes several existing state-of-the-art recommendation algorithms, and address the efficiency issue by investigating sampling strategies in the stochastic gradient descent training for the framework.

Collaborative Filtering

Joint Text Embedding for Personalized Content-based Recommendation

no code implementations4 Jun 2017 Ting Chen, Liangjie Hong, Yue Shi, Yizhou Sun

While latent factors of items can be learned effectively from user interaction data, in many cases, such data is not available, especially for newly emerged items.

News Recommendation Recommendation Systems

Ideology Detection for Twitter Users with Heterogeneous Types of Links

no code implementations24 Dec 2016 Yupeng Gu, Ting Chen, Yizhou Sun, Bingyu Wang

The problem of ideology detection is to study the latent (political) placement for people, which is traditionally studied on politicians according to their voting behaviors.

Social and Information Networks

Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

1 code implementation8 Dec 2016 Ting Chen, Yizhou Sun

To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model.

Feature Engineering Network Embedding

Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events

no code implementations26 Aug 2016 Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang

Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.

Anomaly Detection

Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models

no code implementations NeurIPS 2009 Jing Gao, Feng Liang, Wei Fan, Yizhou Sun, Jiawei Han

First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.

General Classification

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