b'Jianfeng Lu'

On the Representation of Solutions to Elliptic PDEs in Barron Spaces

Numerical solutions to high-dimensional partial differential equations (...
06/14/2021 ∙ by Ziang Chen, et al. ∙ 0 ∙ share

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A Priori Generalization Error Analysis of Two-Layer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems

This paper analyzes the generalization error of two-layer neural network...
05/04/2021 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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Incorporating Orientations into End-to-end Driving Model for Steering Control

In this paper, we present a novel end-to-end deep neural network model f...
03/10/2021 ∙ by Peng Wan, et al. ∙ 0 ∙ share

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Structure-preserving numerical schemes for Lindblad equations

We study a family of structure-preserving deterministic numerical scheme...
03/01/2021 ∙ by Yu Cao, et al. ∙ 0 ∙ share

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Actor-Critic 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...
02/22/2021 ∙ by Mo Zhou, et al. ∙ 0 ∙ share

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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...
01/05/2021 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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On the global convergence of randomized coordinate gradient descent for non-convex optimization

In this work, we analyze the global convergence property of coordinate g...
01/05/2021 ∙ by Ziang Chen, et al. ∙ 0 ∙ share

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Complexity of zigzag sampling algorithm for strongly log-concave distributions

We study the computational complexity of zigzag sampling algorithm for s...
12/21/2020 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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Neural Collapse with Cross-Entropy Loss

We consider the variational problem of cross-entropy loss with n feature...
12/15/2020 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising

Deep learning (DL) based hyperspectral images (HSIs) denoising approache...
12/03/2020 ∙ by Fengchao Xiong, et al. ∙ 0 ∙ share

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Manifold Learning and Nonlinear Homogenization

We describe an efficient domain decomposition-based framework for nonlin...
11/01/2020 ∙ by Shi Chen, et al. ∙ 0 ∙ share

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Fast localization of eigenfunctions via smoothed potentials

We study the problem of predicting highly localized low-lying eigenfunct...
10/28/2020 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime

We study the problem of policy optimization for infinite-horizon discoun...
10/22/2020 ∙ by Andrea Agazzi, et al. ∙ 0 ∙ share

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Random Coordinate Underdamped Langevin Monte Carlo

The Underdamped Langevin Monte Carlo (ULMC) is a popular Markov chain Mo...
10/22/2020 ∙ by Zhiyan Ding, et al. ∙ 0 ∙ share

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Random Coordinate Langevin Monte Carlo

Langevin Monte Carlo (LMC) is a popular Markov chain Monte Carlo samplin...
10/03/2020 ∙ by Zhiyan Ding, et al. ∙ 4 ∙ share

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Efficient sampling from the Bingham distribution

We give a algorithm for exact sampling from the Bingham distribution p(x...
09/30/2020 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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On explicit L^2-convergence rate estimate for piecewise deterministic Markov processes

We establish L^2-exponential convergence rate for three popular piecewis...
07/29/2020 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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Neural Machine Translation with Error Correction

Neural machine translation (NMT) generates the next target token given a...
07/21/2020 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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A Proximal-Gradient Algorithm for Crystal Surface Evolution

As a counterpoint to recent numerical methods for crystal surface evolut...
06/22/2020 ∙ by Katy Craig, et al. ∙ 0 ∙ share

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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...
06/21/2020 ∙ by Cheng Cheng, et al. ∙ 0 ∙ share

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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...
06/13/2020 ∙ by Zhenning Cai, et al. ∙ 0 ∙ share

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LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning

While pre-training and fine-tuning, e.g., BERT <cit.>, GPT-2 <cit.>, hav...
04/27/2020 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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MPNet: Masked and Permuted Pre-training for Language Understanding

BERT adopts masked language modeling (MLM) for pre-training and is one o...
04/20/2020 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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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...
04/19/2020 ∙ by Yulong Lu, et al. ∙ 0 ∙ share

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Posterior computation with the Gibbs zig-zag sampler

Markov chain Monte Carlo (MCMC) sampling algorithms have dominated the l...
04/08/2020 ∙ by Matthias Sachs, et al. ∙ 0 ∙ share

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Complexity of randomized algorithms for underdamped Langevin dynamics

We establish an information complexity lower bound of randomized algorit...
03/22/2020 ∙ by Yu Cao, et al. ∙ 0 ∙ share

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Existence and computation of generalized Wannier functions for non-periodic systems in two dimensions and higher

Exponentially-localized Wannier functions (ELWFs) are a basis of the Fer...
03/14/2020 ∙ by Kevin D. Stubbs, et al. ∙ 0 ∙ share

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A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth

Training deep neural networks with stochastic gradient descent (SGD) can...
03/11/2020 ∙ by Yiping Lu, et al. ∙ 42 ∙ share

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Ensemble Kalman Inversion for nonlinear problems: weights, consistency, and variance bounds

Ensemble Kalman Inversion (EnKI), originally derived from Enseble Kalman...
03/04/2020 ∙ by Zhiyan Ding, et al. ∙ 0 ∙ share

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Solving high-dimensional 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...
02/07/2020 ∙ by Jiequn Han, et al. ∙ 0 ∙ share

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Deep Network Approximation for Smooth Functions

This paper establishes optimal approximation error characterization of d...
01/09/2020 ∙ by Jianfeng Lu, et al. ∙ 0 ∙ share

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Non-Convex Planar Harmonic Maps

We formulate a novel characterization of a family of invertible maps bet...
01/05/2020 ∙ by Shahar Z. Kovalsky, et al. ∙ 0 ∙ share

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Universal approximation of symmetric and anti-symmetric functions

We consider universal approximations of symmetric and anti-symmetric fun...
12/04/2019 ∙ by Jiequn Han, et al. ∙ 0 ∙ share

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Part-based Multi-stream Model for Vehicle Searching

Due to the enormous requirement in public security and intelligent trans...
11/11/2019 ∙ by Ya Sun, et al. ∙ 17 ∙ share

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Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds

Estimating the normalizing constant of an unnormalized probability distr...
11/08/2019 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Fisher information regularization schemes for Wasserstein gradient flows

We propose a variational scheme for computing Wasserstein gradient flows...
07/03/2019 ∙ by Wuchen Li, et al. ∙ 1 ∙ share

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Efficient posterior sampling for high-dimensional imbalanced logistic regression

High-dimensional data are routinely collected in many application areas....
05/27/2019 ∙ by Deborshee Sen, et al. ∙ 0 ∙ share

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Temporal-difference learning for nonlinear value function approximation in the lazy training regime

We discuss the approximation of the value function for infinite-horizon ...
05/27/2019 ∙ by Andrea Agazzi, et al. ∙ 0 ∙ share

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Accelerating Langevin Sampling with Birth-death

A fundamental problem in Bayesian inference and statistical machine lear...
05/23/2019 ∙ by Yulong Lu, et al. ∙ 0 ∙ share

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Tensor Ring Decomposition: Energy Landscape and One-loop Convergence of Alternating Least Squares

In this work, we study the tensor ring decomposition and its associated ...
05/17/2019 ∙ by Ziang Chen, et al. ∙ 0 ∙ share

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Variational training of neural network approximations of solution maps for physical models

A novel solve-training framework is proposed to train neural network in ...
05/07/2019 ∙ by Yingzhou Li, et al. ∙ 0 ∙ share

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MASS: Masked Sequence to Sequence Pre-training for Language Generation

Pre-training and fine-tuning, e.g., BERT, have achieved great success in...
05/07/2019 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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Tensorization of the strong data processing inequality for quantum chi-square divergences

Quantifying the contraction of classical and quantum states under noisy ...
04/13/2019 ∙ by Yu Cao, et al. ∙ 0 ∙ share

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Generating Adversarial Examples With Conditional Generative Adversarial Net

Recently, deep neural networks have significant progress and successful ...
03/18/2019 ∙ by Ping Yu, et al. ∙ 0 ∙ share

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A stochastic version of Stein Variational Gradient Descent for efficient sampling

We propose in this work RBM-SVGD, a stochastic version of Stein Variatio...
02/09/2019 ∙ by Lei Li, et al. ∙ 0 ∙ share

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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...
12/15/2018 ∙ by Liantao Wang, et al. ∙ 0 ∙ share

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Hybrid Self-Attention Network for Machine Translation

The encoder-decoder is the typical framework for Neural Machine Translat...
11/01/2018 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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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...
09/13/2018 ∙ by Anton Martinsson, et al. ∙ 0 ∙ share

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Double Path Networks for Sequence to Sequence Learning

Encoder-decoder based Sequence to Sequence learning (S2S) has made remar...
06/13/2018 ∙ by Kaitao Song, et al. ∙ 0 ∙ share

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Stochastic modified equations for the asynchronous stochastic gradient descent

We propose a stochastic modified equations (SME) for modeling the asynch...
05/21/2018 ∙ by Jing An, et al. ∙ 0 ∙ share

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Jianfeng Lu

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