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07/11/2023
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
The time evolution of physical systems is described by differential equa...
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06/20/2023
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
Neural networks (NNs) that exploit strong inductive biases based on phys...
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06/14/2023
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
Modelling spatio-temporal processes on road networks is a task of growin...
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06/07/2023
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Graph neural networks (GNNs) have various practical applications, such a...
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06/06/2023
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Deep graph generative modeling has gained enormous attraction in recent ...
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06/06/2023
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Graph neural networks (GNNs), in general, are built on the assumption of...
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01/29/2023
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Optimization of atomic structures presents a challenging problem, due to...
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11/10/2022
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
Recently, graph neural networks have been gaining a lot of attention to ...
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10/21/2022
Global Counterfactual Explainer for Graph Neural Networks
Graph neural networks (GNNs) find applications in various domains such a...
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09/23/2022
Learning Rigid Body Dynamics with Lagrangian Graph Neural Network
Lagrangian and Hamiltonian neural networks (LNN and HNN respectively) en...
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09/22/2022
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
Neural networks with physics based inductive biases such as Lagrangian n...
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09/03/2022
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
Physical systems are commonly represented as a combination of particles,...
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08/24/2022
Lifelong Learning for Neural powered Mixed Integer Programming
Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bou...
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05/07/2022
Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers
With the increasing popularity of food delivery platforms, it has become...
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03/16/2022
FairFoody: Bringing in Fairness in Food Delivery
Along with the rapid growth and rise to prominence of food delivery plat...
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03/07/2022
TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
There has been a recent surge in learning generative models for graphs. ...
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12/25/2021
Task and Model Agnostic Adversarial Attack on Graph Neural Networks
Graph neural networks (GNNs) have witnessed significant adoption in the ...
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12/24/2021
A Neural Framework for Learning Subgraph and Graph Similarity Measures
Subgraph similarity search is a fundamental operator in graph analysis. ...
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10/07/2021
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
Realistic models of physical world rely on differentiable symmetries tha...
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08/29/2020
Batching and Matching for Food Delivery in Dynamic Road Networks
Given a stream of food orders and available delivery vehicles, how shoul...
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08/19/2020
GraphReach: Locality-Aware Graph Neural Networks using Reachability Estimations
Analyzing graphs by representing them in a low dimensional space using G...
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01/22/2020
GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation
Graph generative models have been extensively studied in the data mining...
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03/08/2019
Learning Heuristics over Large Graphs via Deep Reinforcement Learning
In this paper, we propose a deep reinforcement learning framework called...
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01/19/2018
Identifying User Intent and Context in Graph Queries
Graph querying is the task of finding similar embeddings of a given quer...
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01/19/2018