
On the Representation of Solutions to Elliptic PDEs in Barron Spaces
Numerical solutions to highdimensional partial differential equations (...
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A Priori Generalization Error Analysis of TwoLayer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems
This paper analyzes the generalization error of twolayer neural network...
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Incorporating Orientations into Endtoend Driving Model for Steering Control
In this paper, we present a novel endtoend deep neural network model f...
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Structurepreserving numerical schemes for Lindblad equations
We study a family of structurepreserving deterministic numerical scheme...
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ActorCritic Method for High Dimensional Static Hamilton–Jacobi–Bellman Partial Differential Equations based on Neural Networks
We propose a novel numerical method for high dimensional Hamilton–Jacobi...
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A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations
This paper concerns the a priori generalization analysis of the Deep Rit...
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On the global convergence of randomized coordinate gradient descent for nonconvex optimization
In this work, we analyze the global convergence property of coordinate g...
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Complexity of zigzag sampling algorithm for strongly logconcave distributions
We study the computational complexity of zigzag sampling algorithm for s...
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Neural Collapse with CrossEntropy Loss
We consider the variational problem of crossentropy loss with n feature...
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SMDSNet: Model Guided SpectralSpatial Network for Hyperspectral Image Denoising
Deep learning (DL) based hyperspectral images (HSIs) denoising approache...
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Manifold Learning and Nonlinear Homogenization
We describe an efficient domain decompositionbased framework for nonlin...
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Fast localization of eigenfunctions via smoothed potentials
We study the problem of predicting highly localized lowlying eigenfunct...
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Global optimality of softmax policy gradient with single hidden layer neural networks in the meanfield regime
We study the problem of policy optimization for infinitehorizon discoun...
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Random Coordinate Underdamped Langevin Monte Carlo
The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Mo...
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Random Coordinate Langevin Monte Carlo
Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo samplin...
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Efficient sampling from the Bingham distribution
We give a algorithm for exact sampling from the Bingham distribution p(x...
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On explicit L^2convergence rate estimate for piecewise deterministic Markov processes
We establish L^2exponential convergence rate for three popular piecewis...
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Neural Machine Translation with Error Correction
Neural machine translation (NMT) generates the next target token given a...
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A ProximalGradient Algorithm for Crystal Surface Evolution
As a counterpoint to recent numerical methods for crystal surface evolut...
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Stable Phase Retrieval from Locally Stable and Conditionally Connected Measurements
This paper is concerned with stable phase retrieval for a family of phas...
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Numerical analysis for inchworm Monte Carlo method: Sign problem and error growth
We consider the numerical analysis of the inchworm Monte Carlo method, w...
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LightPAFF: A TwoStage Distillation Framework for Pretraining and Finetuning
While pretraining and finetuning, e.g., BERT <cit.>, GPT2 <cit.>, hav...
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MPNet: Masked and Permuted Pretraining for Language Understanding
BERT adopts masked language modeling (MLM) for pretraining and is one o...
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A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions
This paper studies the universal approximation property of deep neural n...
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Posterior computation with the Gibbs zigzag sampler
Markov chain Monte Carlo (MCMC) sampling algorithms have dominated the l...
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Complexity of randomized algorithms for underdamped Langevin dynamics
We establish an information complexity lower bound of randomized algorit...
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Existence and computation of generalized Wannier functions for nonperiodic systems in two dimensions and higher
Exponentiallylocalized Wannier functions (ELWFs) are a basis of the Fer...
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A Meanfield Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth
Training deep neural networks with stochastic gradient descent (SGD) can...
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Ensemble Kalman Inversion for nonlinear problems: weights, consistency, and variance bounds
Ensemble Kalman Inversion (EnKI), originally derived from Enseble Kalman...
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Solving highdimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach
We propose a new method to solve eigenvalue problems for linear and semi...
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Deep Network Approximation for Smooth Functions
This paper establishes optimal approximation error characterization of d...
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NonConvex Planar Harmonic Maps
We formulate a novel characterization of a family of invertible maps bet...
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Universal approximation of symmetric and antisymmetric functions
We consider universal approximations of symmetric and antisymmetric fun...
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Partbased Multistream Model for Vehicle Searching
Due to the enormous requirement in public security and intelligent trans...
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Estimating Normalizing Constants for LogConcave Distributions: Algorithms and Lower Bounds
Estimating the normalizing constant of an unnormalized probability distr...
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Fisher information regularization schemes for Wasserstein gradient flows
We propose a variational scheme for computing Wasserstein gradient flows...
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Efficient posterior sampling for highdimensional imbalanced logistic regression
Highdimensional data are routinely collected in many application areas....
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Temporaldifference learning for nonlinear value function approximation in the lazy training regime
We discuss the approximation of the value function for infinitehorizon ...
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Accelerating Langevin Sampling with Birthdeath
A fundamental problem in Bayesian inference and statistical machine lear...
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Tensor Ring Decomposition: Energy Landscape and Oneloop Convergence of Alternating Least Squares
In this work, we study the tensor ring decomposition and its associated ...
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Variational training of neural network approximations of solution maps for physical models
A novel solvetraining framework is proposed to train neural network in ...
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MASS: Masked Sequence to Sequence Pretraining for Language Generation
Pretraining and finetuning, e.g., BERT, have achieved great success in...
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Tensorization of the strong data processing inequality for quantum chisquare divergences
Quantifying the contraction of classical and quantum states under noisy ...
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Generating Adversarial Examples With Conditional Generative Adversarial Net
Recently, deep neural networks have significant progress and successful ...
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A stochastic version of Stein Variational Gradient Descent for efficient sampling
We propose in this work RBMSVGD, a stochastic version of Stein Variatio...
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Weakly supervised segment annotation via expectation kernel density estimation
Since the labelling for the positive images/videos is ambiguous in weakl...
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Hybrid SelfAttention Network for Machine Translation
The encoderdecoder is the typical framework for Neural Machine Translat...
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Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning
We investigate the theoretical foundations of the simulated tempering me...
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Double Path Networks for Sequence to Sequence Learning
Encoderdecoder based Sequence to Sequence learning (S2S) has made remar...
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Stochastic modified equations for the asynchronous stochastic gradient descent
We propose a stochastic modified equations (SME) for modeling the asynch...
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Jianfeng Lu
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