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Found 24 papers, 9 papers with code
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task.
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
Data-driven modeling of fluid dynamics has advanced rapidly with neural PDE solvers, yet a fair and strong benchmark remains fragmented due to the absence of unified PDE datasets and standardized evaluation protocols.
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs.
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection
We introduce an evaluation of a comprehensive collection of decoding-free candidate selection approaches on a comprehensive set of tasks, including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with a massive amount of candidates, some with 10k+ options.
Predicting Time Series of Networked Dynamical Systems without Knowing Topology
To address these gaps, we propose a novel framework for learning network dynamics directly from observed time-series data, when prior knowledge of graph topology or governing dynamical equations is absent.
Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural architecture search.
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics
Accurately predicting long-horizon molecular dynamics (MD) trajectories remains a significant challenge, as existing deep learning methods often struggle to retain fidelity over extended simulations.
Segmentation-aware Prior Assisted Joint Global Information Aggregated 3D Building Reconstruction
These plane priors, combined with triangulation priors, form a reliable prior candidate set.
Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling
While TRS is a domain-specific physical prior, we present the first theoretical proof that TRS loss can universally improve modeling accuracy by minimizing higher-order Taylor terms in ODE integration, which is numerically beneficial to various systems regardless of their properties, even for irreversible systems.
Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
This paper explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) tasks through Machine Learning (ML) techniques.
MIRAI: Evaluating LLM Agents for Event Forecasting
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.
BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations
Brain network analysis is vital for understanding the neural interactions regarding brain structures and functions, and identifying potential biomarkers for clinical phenotypes.
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.
