
Learning Collaborative Policies to Solve NPhard Routing Problems
Recently, deep reinforcement learning (DRL) frameworks have shown potent...
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ContinuousDepth Neural Models for Dynamic Graph Prediction
We introduce the framework of continuousdepth graph neural networks (GN...
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Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Effective control and prediction of dynamical systems often require appr...
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Differentiable Multiple Shooting Layers
We detail a novel class of implicit neural models. Leveraging timeparal...
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Learning Stochastic Optimal Policies via Gradient Descent
We systematically develop a learningbased treatment of stochastic optim...
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ScheduleNet: Learn to solve multiagent scheduling problems with reinforcement learning
We propose ScheduleNet, a RLbased realtime scheduler, that can solve v...
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Convergent Graph Solvers
We propose the convergent graph solver (CGS), a deep learning method tha...
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Learning to schedule jobshop problems: Representation and policy learning using graph neural network and reinforcement learning
We propose a framework to learn to schedule a jobshop problem (JSSP) us...
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A Hypergraph Convolutional Neural Network for Molecular Properties Prediction using Functional Group
We propose a Molecular Hypergraph Convolutional Network (MolHGCN) that p...
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Cooperative and Competitive Biases for MultiAgent Reinforcement Learning
Training a multiagent reinforcement learning (MARL) algorithm is more c...
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Optimal Energy Shaping via Neural Approximators
We introduce optimal energy shaping as an enhancement of classical passi...
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TorchDyn: A Neural Differential Equations Library
Continuousdepth learning has recently emerged as a novel perspective on...
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REMAX: Relational Representation for MultiAgent Exploration
Training a multiagent reinforcement learning (MARL) model is generally ...
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Hypersolvers: Toward Fast ContinuousDepth Models
The infinitedepth paradigm pioneered by Neural ODEs has launched a rena...
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Approximate Inference for Spectral Mixture Kernel
A spectral mixture (SM) kernel is a flexible kernel used to model any st...
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Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential e...
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Dissecting Neural ODEs
Continuous deep learning architectures have recently reemerged as varia...
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Scalable Hybrid HMM with Gaussian Process Emission for Sequential Timeseries Data Clustering
Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission c...
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Graph Neural Ordinary Differential Equations
We extend the framework of graph neural networks (GNN) to continuous tim...
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MultiAgent ActorCritic with Hierarchical Graph Attention Network
Most previous studies on multiagent reinforcement learning focus on der...
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WATTNet: Learning to Trade FX via Hierarchical SpatioTemporal Representation of Highly Multivariate Time Series
Finance is a particularly challenging application area for deep learning...
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PortHamiltonian Approach to Neural Network Training
Neural networks are discrete entities: subdivided into discrete layers a...
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Learning scalable and transferable multirobot/machine sequential assignment planning via graph embedding
Can the success of reinforcement learning methods for simple combinatori...
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Scalable and transferable learning of algorithms via graph embedding for multirobot reward collection
Can the success of reinforcement learning methods for combinatorial opti...
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MultiAgent ActorCritic with Generative Cooperative Policy Network
We propose an efficient multiagent reinforcement learning approach to d...
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Jinkyoo Park
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