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08/28/2023
A convergent interacting particle method for computing KPP front speeds in random flows
We aim to efficiently compute spreading speeds of reaction-diffusion-adv...
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07/02/2023
A Proximal Algorithm for Network Slimming
As a popular channel pruning method for convolutional neural networks (C...
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07/02/2023
Fourier-Mixed Window Attention: Accelerating Informer for Long Sequence Time-Series Forecasting
We study a fast local-global window-based attention method to accelerate...
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07/01/2023
Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising
Poisson noise commonly occurs in images captured by photon-limited imagi...
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04/18/2023
An Augmented Subspace Based Adaptive Proper Orthogonal Decomposition Method for Time Dependent Partial Differential Equations
In this paper, we propose an augmented subspace based adaptive proper or...
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02/10/2023
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
In this paper, we propose a feature affinity (FA) assisted knowledge dis...
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01/06/2023
Efficient Image Segmentation Framework with Difference of Anisotropic and Isotropic Total Variation for Blur and Poisson Noise Removal
In this paper, we aim to segment an image degraded by blur and Poisson n...
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08/31/2022
A DeepParticle method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems
We study a regularized interacting particle method for computing aggrega...
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04/19/2022
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Learning neural ODEs often requires solving very stiff ODE systems, prim...
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04/16/2022
Searching Intrinsic Dimensions of Vision Transformers
It has been shown by many researchers that transformers perform as well ...
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04/09/2022
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method
We propose an adaptive projection-gradient descent-shrinkage-splitting m...
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03/30/2022
Enhancing Zero-Shot Many to Many Voice Conversion with Self-Attention VAE
Variational auto-encoder(VAE) is an effective neural network architectur...
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02/21/2022
A Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation
In this paper, we propose a multi-stage image segmentation framework tha...
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01/23/2022
An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
We developed an integrated recurrent neural network and nonlinear regres...
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01/22/2022
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
We propose GLassoformer, a novel and efficient transformer architecture ...
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12/08/2021
Synchronized Front Propagation and Delayed Flame Quenching in Strain G-equation and Time-Periodic Cellular Flows
G-equations are level-set type Hamilton-Jacobi partial differential equa...
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11/02/2021
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method
We introduce the so called DeepParticle method to learn and generate inv...
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06/02/2021
Convergence analysis of a Lagrangian numerical scheme in computing effective diffusivity of 3D time-dependent flows
In this paper, we study the convergence analysis for a robust stochastic...
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03/27/2021
A convergent interacting particle method and computation of KPP front speeds in chaotic flows
In this paper, we study the propagation speeds of reaction-diffusion-adv...
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12/21/2020
A Semi-Lagrangian Computation of Front Speeds of G-equation in ABC and Kolmogorov Flows with Estimation via Ballistic Orbits
The Arnold-Beltrami-Childress (ABC) flow and the Kolmogorov flow are thr...
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12/10/2020
Recurrence of Optimum for Training Weight and Activation Quantized Networks
Deep neural networks (DNNs) are quantized for efficient inference on res...
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11/23/2020
Learning Quantized Neural Nets by Coarse Gradient Method for Non-linear Classification
Quantized or low-bit neural networks are attractive due to their inferen...
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10/18/2020
A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19
As the COVID-19 pandemic evolves, reliable prediction plays an important...
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10/03/2020
Nonconvex Regularization for Network Slimming:Compressing CNNs Even More
In the last decade, convolutional neural networks (CNNs) have evolved to...
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09/04/2020
Lorentzian Peak Sharpening and Sparse Blind Source Separation for NMR Spectroscopy
In this paper, we introduce a preprocessing technique for blind source s...
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08/31/2020
An Integrated Approach to Produce Robust Models with High Efficiency
Deep Neural Networks (DNNs) needs to be both efficient and robust for pr...
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08/10/2020
RARTS: a Relaxed Architecture Search Method
Differentiable architecture search (DARTS) is an effective method for da...
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07/14/2020
A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19
The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the w...
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05/09/2020
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation
In a class of piecewise-constant image segmentation models, we incorpora...
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02/28/2020
Global Convergence and Geometric Characterization of Slow to Fast Weight Evolution in Neural Network Training for Classifying Linearly Non-Separable Data
In this paper, we study the dynamics of gradient descent in learning neu...
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12/17/2019
ℓ_0 Regularized Structured Sparsity Convolutional Neural Networks
Deepening and widening convolutional neural networks (CNNs) significantl...
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09/27/2019
Computing Residual Diffusivity by Adaptive Basis Learning via Super-Resolution Deep Neural Networks
It is expensive to compute residual diffusivity in chaotic in-compressib...
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06/24/2019
Two-Grid based Adaptive Proper Orthogonal Decomposition Algorithm for Time Dependent Partial Differential Equations
In this article, we propose a two-grid based adaptive proper orthogonal ...
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06/21/2019
Convergence of stochastic structure-preserving schemes for computing effective diffusivity in random flows
In this paper, we propose stochastic structure-preserving schemes to com...
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03/13/2019
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
Training activation quantized neural networks involves minimizing a piec...
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02/20/2019
Learning Sparse Neural Networks via ℓ_0 and Tℓ_1 by a Relaxed Variable Splitting Method with Application to Multi-scale Curve Classification
We study sparsification of convolutional neural networks (CNN) by a rela...
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02/13/2019
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
We study epidemic forecasting on real-world health data by a graph-struc...
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01/24/2019
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks
ShuffleNet is a state-of-the-art light weight convolutional neural netwo...
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08/15/2018
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks
Quantized deep neural networks (QDNNs) are attractive due to their much ...
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01/19/2018
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
We propose BinaryRelax, a simple two-phase algorithm, for training deep ...
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11/23/2017
Deep Learning for Real-Time Crime Forecasting and its Ternarization
Real-time crime forecasting is important. However, accurate prediction o...
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01/03/2013
A Method for Finding Structured Sparse Solutions to Non-negative Least Squares Problems with Applications
Demixing problems in many areas such as hyperspectral imaging and differ...
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02/04/2011
