
MODNet: A Machine Learning Approach via ModelOperatorData Network for Solving PDEs
In this paper, we propose a modeloperatordata network (MODNet) for so...
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Privacy Budget Scheduling
Machine learning (ML) models trained on personal data have been shown to...
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Embedding Principle of Loss Landscape of Deep Neural Networks
Understanding the structure of loss landscape of deep neural networks (D...
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DTNN: Energyefficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications
Deep neural networks (DNN) have achieved remarkable success in computer ...
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Towards Understanding the Condensation of Twolayer Neural Networks at Initial Training
It is important to study what implicit regularization is imposed on the ...
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An Upper Limit of Decaying Rate with Respect to Frequency in Deep Neural Network
Deep neural network (DNN) usually learns the target function from low to...
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Efficient Spiking Neural Networks with Radix Encoding
Spiking neural networks (SNNs) have advantages in latency and energy eff...
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Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks
In an attempt to better understand structural benefits and generalizatio...
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RCT: Resource Constrained Training for Edge AI
Neural networks training on edge terminals is essential for edge AI comp...
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QROSS: QUBO Relaxation Parameter Optimisation via Learning Solver Surrogates
An increasingly popular method for solving a constrained combinatorial o...
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Convergence from Atomistic Model to PeierlsNabarro Model for Dislocations in Bilayer System with Complex Lattice
In this paper, we prove the convergence from the atomistic model to the ...
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Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks
Why heavily parameterized neural networks (NNs) do not overfit the data ...
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MetaReinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
In this paper, the problem of enhancing the quality of virtual reality (...
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SGPBFT: a Secure and Highly Efficient Blockchain PBFT Consensus Algorithm for Internet of Vehicles
The Internet of Vehicles (IoV) is an application of the Internet of thin...
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Adaptive Precision Training for Resource Constrained Devices
Learn insitu is a growing trend for Edge AI. Training deep neural netwo...
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Fourierdomain Variational Formulation and Its Wellposedness for Supervised Learning
A supervised learning problem is to find a function in a hypothesis func...
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On the exact computation of linear frequency principle dynamics and its generalization
Recent works show an intriguing phenomenon of Frequency Principle (FPri...
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A regularized deep matrix factorized model of matrix completion for image restoration
It has been an important approach of using matrix completion to perform ...
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Phase diagram for twolayer ReLU neural networks at infinitewidth limit
How neural network behaves during the training over different choices of...
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Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH)
Gradient descent yields zero training loss in polynomial time for deep n...
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TwoLayer Neural Networks for Partial Differential Equations: Optimization and Generalization Theory
Deep learning has significantly revolutionized the design of numerical a...
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EDCompress: EnergyAware Model Compression with Dataflow
Edge devices demand low energy consumption, cost and small form factor. ...
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Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVs
In this paper, the problem of optimizing the deployment of unmanned aeri...
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Theory of the Frequency Principle for General Deep Neural Networks
Along with fruitful applications of Deep Neural Networks (DNNs) to reali...
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Explicitizing an Implicit Bias of the Frequency Principle in Twolayer Neural Networks
It remains a puzzle that why deep neural networks (DNNs), with more para...
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A type of generalization error induced by initialization in deep neural networks
How different initializations and loss functions affect the learning of ...
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Frequency Principle: Fourier Analysis Sheds Light on Deep Neural Networks
We study the training process of Deep Neural Networks (DNNs) from the Fo...
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Tao Luo
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