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2020-10
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
Predicting Infectiousness for Proactive Contact Tracing
GraphMix: Improved Training of GNNs for Semi-Supervised Learning.
2020-08
Robustesse structurelle des architectures d'apprentissage profond
2020-07
GRADE: Graph Dynamic Embedding.
Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs
Continuous Graph Neural Networks
A Graph to Graphs Framework for Retrosynthesis Prediction
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
2020-06
Graph Policy Network for Transferable Active Learning on Graphs
Graph Policy Network for Transferable Active Learning on Graphs
2020-05
COVI White Paper.
Deep Geometric Knowledge Distillation with Graphs
ICASSP 2020
(2020-05-04)
hal.archives-ouvertes.frPDF[Also on arXiv preprint arXiv:1911.03080 (2019-11-08)]2020-04
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
2020-02
Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games.
2020-01
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
2019-12
Probabilistic Logic Neural Networks for Reasoning
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
2019-11
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
GRLA 2019: The first International Workshop on Graph Representation Learning and its Applications
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
2019-09
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention
Transfer Active Learning For Graph Neural Networks
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
2019-08
An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation
Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data
(2019-08-05)
ui.adsabs.harvard.edu[LATEST on arXiv preprint arXiv:1908.04032 (2019-08-12)]Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks.
2019-07
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Weakly-supervised Knowledge Graph Alignment with Adversarial Learning
2019-06
Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning
Learning Powerful Policies by Using Consistent Dynamics Model.
GMNN: Graph Markov Neural Networks
Structural Robustness for Deep Learning Architectures
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
Structural Robustness for Deep Learning Architectures
2019-05
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
2019-03
Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction.
AMIA 2019
(2019-03-01)
ui.adsabs.harvard.eduPDF[LATEST on arXiv preprint arXiv:1903.08652 (2019-03-20)]2019-01
Session-Based Social Recommendation via Dynamic Graph Attention Networks
Attending Over Triads for Learning Signed Network Embedding
2018-09
Learning powerful policies and better dynamics models by encouraging consistency
Data Poisoning Attack against Unsupervised Node Embedding Methods
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