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Found 12 papers, 4 papers with code
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time.
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Given a set of candidate entities (e. g. movie titles), the ability to identify similar entities is a core capability of many recommender systems.
TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems
Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling.
MedChatZH: a Better Medical Adviser Learns from Better Instructions
Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems.
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems
We model a multi-agent dynamical system as a graph and propose CounterFactual GraphODE (CF-GODE), a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units.
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Hence, those models are unable to predict further future.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.
Ranked #3 on
Knowledge Graph Completion
on DPB-5L (French)
Coupled Graph ODE for Learning Interacting System Dynamics
On one hand, features of objects change over time, influenced by the linked objects in the interaction graph.
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations
In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time.
