
Optimising Stochastic Routing for Taxi Fleets with Model Enhanced Reinforcement Learning
The future of mobilityasaService (Maas)should embrace an integrated s...
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On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis
We study the approximation properties and optimization dynamics of recur...
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OnsagerNet: Learning Stable and Interpretable Dynamics using a Generalized Onsager Principle
We propose a systematic method for learning stable and interpretable dyn...
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Inverse design of crystals using generalized invertible crystallographic representation
Deep learning has fostered many novel applications in materials informat...
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Optimization in Machine Learning: A Distribution Space Approach
We present the viewpoint that optimization problems encountered in machi...
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Collaborative Inference for Efficient Remote Monitoring
While current machine learning models have impressive performance over a...
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Deep Learning via Dynamical Systems: An Approximation Perspective
We build on the dynamical systems approach to deep learning, where deep ...
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Computing Committor Functions for the Study of Rare Events Using Deep Learning
The committor function is a central object of study in understanding tra...
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Distributed Optimization for OverParameterized Learning
Distributed optimization often consists of two updating phases: local op...
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Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
We develop the mathematical foundations of the stochastic modified equat...
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On the Convergence and Robustness of Batch Normalization
Despite its empirical success, the theoretical underpinnings of the stab...
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Dynamics of Taxilike Logistics Systems: Theory and Microscopic Simulations
In this paper we study the dynamics of a class of biagent logistics sys...
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A MeanField Optimal Control Formulation of Deep Learning
Recent work linking deep neural networks and dynamical systems opened up...
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An Optimal Control Approach to Deep Learning and Applications to DiscreteWeight Neural Networks
Deep learning is formulated as a discretetime optimal control problem. ...
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Maximum Principle Based Algorithms for Deep Learning
The continuous dynamical system approach to deep learning is explored in...
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Stochastic modified equations and adaptive stochastic gradient algorithms
We develop the method of stochastic modified equations (SME), in which s...
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Qianxiao Li
verfied profile
Assistant Professor at National University of Singapore