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06/02/2023
Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Learning an effective global model on private and decentralized datasets...
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05/18/2023
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Automatically generating human-readable text describing the functionalit...
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05/09/2023
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Class imbalance problems widely exist in the medical field and heavily d...
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02/27/2023
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Graph neural networks (GNNs) have shown high potential for a variety of ...
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09/15/2022
Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description
Most existing Time series classification (TSC) models lack interpretabil...
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08/09/2022
Attention Hijacking in Trojan Transformers
Trojan attacks pose a severe threat to AI systems. Recent works on Trans...
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05/21/2022
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Towards real-world information extraction scenario, research of relation...
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05/13/2022
A Study of the Attention Abnormality in Trojaned BERTs
Trojan attacks raise serious security concerns. In this paper, we invest...
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02/16/2022
When Does A Spectral Graph Neural Network Fail in Node Classification?
Spectral Graph Neural Networks (GNNs) with various graph filters have re...
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01/28/2022
Neural Approximation of Extended Persistent Homology on Graphs
Persistent homology is a widely used theory in topological data analysis...
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10/06/2021
A Topological View of Rule Learning in Knowledge Graphs
Inductive relation prediction is an important learning task for knowledg...
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07/10/2021
Improving Inductive Link Prediction Using Hyper-Relational Facts
For many years, link prediction on knowledge graphs (KGs) has been a pur...
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04/11/2021
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Contrastive Learning has emerged as a powerful representation learning m...
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02/20/2021
Persistence Homology for Link Prediction: An Interactive View
Link prediction is an important learning task for graph-structured data....
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02/06/2021
Wasserstein diffusion on graphs with missing attributes
Missing node attributes is a common problem in real-world graphs. Graph ...
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01/14/2021
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network
Graph convolutional networks have achieved great success on graph-struct...
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11/22/2020
GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks
Graph Neural Networks (GNNs) aim to extend deep learning techniques to g...
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10/25/2020
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Crowdsourcing has attracted much attention for its convenience to collec...
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10/24/2020
Deep Graph Matching and Searching for Semantic Code Retrieval
Code retrieval is to find the code snippet from a large corpus of source...
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10/05/2020
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Evaluation of a document summarization system has been a critical factor...
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07/08/2020
Hierarchical Graph Matching Networks for Deep Graph Similarity Learning
While the celebrated graph neural networks yield effective representatio...
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05/21/2020
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare...
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05/21/2020
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
There have been more than 850,000 confirmed cases and over 48,000 deaths...
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12/24/2019
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical abstractions are a methodology for solving large-scale grap...
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10/04/2019
GENN: Predicting Correlated Drug-drug Interactions with Graph Energy Neural Networks
Gaining more comprehensive knowledge about drug-drug interactions (DDIs)...
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07/17/2019
DeepDrawing: A Deep Learning Approach to Graph Drawing
Node-link diagrams are widely used to facilitate network explorations. H...
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06/02/2019
Pre-training of Graph Augmented Transformers for Medication Recommendation
Medication recommendation is an important healthcare application. It is ...
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05/27/2019
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various ...
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05/15/2019
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of...
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02/26/2019
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
Graph representation learning resurges as a trending research subject ow...
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12/07/2018
Reaching Data Confidentiality and Model Accountability on the CalTrain
Distributed collaborative learning (DCL) paradigms enable building joint...
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11/30/2018
Scalable Graph Learning for Anti-Money Laundering: A First Look
Organized crime inflicts human suffering on a genocidal scale: the Mexic...
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11/01/2018
Adaptive Planner Scheduling with Graph Neural Networks
Automated planning is one of the foundational areas of AI. Since a singl...
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09/11/2018
AWE: Asymmetric Word Embedding for Textual Entailment
Textual entailment is a fundamental task in natural language processing....
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09/07/2018
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Deep generative models have achieved remarkable success in various data ...
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09/06/2018
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we have both rich- and poor- data environments: in a...
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09/06/2018
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domai...
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04/28/2018
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Drug similarity has been studied to support downstream clinical tasks su...
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01/30/2018
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
The graph convolutional networks (GCN) recently proposed by Kipf and Wel...
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12/21/2016
Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data
Topic models have been successfully applied in lexicon extraction. Howev...
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06/30/2016