b'Bao Wang'

research

∙ 08/25/2022

Accelerated Sparse Recovery via Gradient Descent with Nonlinear Conjugate Gradient Momentum

This paper applies an idea of adaptive momentum for the nonlinear conjug...

0 Mengqi Hu, et al. ∙

research

∙ 08/11/2022

Adaptive and Implicit Regularization for Matrix Completion

The explicit low-rank regularization, e.g., nuclear norm regularization,...

33 Zhemin Li, et al. ∙

research

∙ 08/01/2022

Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization

Transformers have achieved remarkable success in sequence modeling and b...

2 Tan Nguyen, et al. ∙

research

∙ 04/19/2022

Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs

Learning neural ODEs often requires solving very stiff ODE systems, prim...

3 Justin Baker, et al. ∙

research

∙ 02/24/2022

Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs

Proper orthogonal decomposition (POD) allows reduced-order modeling of c...

0 Justin Baker, et al. ∙

research

∙ 01/22/2022

glassoformer: a query-sparse transformer for post-fault power grid voltage prediction

We propose GLassoformer, a novel and efficient transformer architecture ...

0 Yunling Zheng, et al. ∙

research

∙ 12/12/2021

Efficient and Reliable Overlay Networks for Decentralized Federated Learning

We propose near-optimal overlay networks based on d-regular expander gra...

0 Yifan Hua, et al. ∙

research

∙ 10/18/2021

Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization

Heavy ball momentum is crucial in accelerating (stochastic) gradient-bas...

14 Tao Sun, et al. ∙

research

∙ 10/13/2021

How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies

We present and review an algorithmic and theoretical framework for impro...

18 Bao Wang, et al. ∙

research

∙ 10/10/2021

Heavy Ball Neural Ordinary Differential Equations

We propose heavy ball neural ordinary differential equations (HBNODEs), ...

15 Hedi Xia, et al. ∙

research

∙ 08/05/2021

FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention

We propose FMMformers, a class of efficient and flexible transformers in...

21 Tan M. Nguyen, et al. ∙

research

∙ 04/23/2021

Decentralized Federated Averaging

Federated averaging (FedAvg) is a communication efficient algorithm for ...

11 Tao Sun, et al. ∙

research

∙ 04/22/2021

Robust Certification for Laplace Learning on Geometric Graphs

Graph Laplacian (GL)-based semi-supervised learning is one of the most u...

1 Matthew Thorpe, et al. ∙

research

∙ 02/02/2021

Stability and Generalization of the Decentralized Stochastic Gradient Descent

The stability and generalization of stochastic gradient-based methods pr...

23 Tao Sun, et al. ∙

research

∙ 12/03/2020

Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum

Momentum plays a crucial role in stochastic gradient-based optimization ...

1 Bao Wang, et al. ∙

research

∙ 11/30/2020

Deep Interactive Denoiser (DID) for X-Ray Computed Tomography

Low dose computed tomography (LDCT) is desirable for both diagnostic ima...

30 Ti Bai, et al. ∙

research

∙ 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...

6 Zhijian Li, et al. ∙

research

∙ 06/12/2020

MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Designing deep neural networks is an art that often involves an expensiv...

62 Tan M. Nguyen, et al. ∙

research

∙ 05/01/2020

Exploring Private Federated Learning with Laplacian Smoothing

Federated learning aims to protect data privacy by collaboratively learn...

6 Zhicong Liang, et al. ∙

research

∙ 03/02/2020

Sparsity Meets Robustness: Channel Pruning for the Feynman-Kac Formalism Principled Robust Deep Neural Nets

Deep neural nets (DNNs) compression is crucial for adaptation to mobile ...

13 Thu Dinh, et al. ∙

research

∙ 02/24/2020

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent

Stochastic gradient descent (SGD) with constant momentum and its variant...

8 Bao Wang, et al. ∙

research

∙ 11/02/2019

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

As an important Markov Chain Monte Carlo (MCMC) method, stochastic gradi...

17 Bao Wang, et al. ∙

research

∙ 07/16/2019

Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning

Improving the accuracy and robustness of deep neural nets (DNNs) and ada...

3 Bao Wang, et al. ∙

research

∙ 06/28/2019

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

Machine learning (ML) models trained by differentially private stochasti...

7 Bao Wang, et al. ∙

research

∙ 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...

27 Zhijian Li, et al. ∙

research

∙ 01/21/2019

A Deterministic Approach to Avoid Saddle Points

Loss functions with a large number of saddle points are one of the main ...

15 Lisa Maria Kreusser, et al. ∙

research

∙ 11/26/2018

EnResNet: ResNet Ensemble via the Feynman-Kac Formalism

We propose a simple yet powerful ResNet ensemble algorithm which consist...

1 Bao Wang, et al. ∙

research

∙ 11/15/2018

Mathematical Analysis of Adversarial Attacks

In this paper, we analyze efficacy of the fast gradient sign method (FGS...

17 Zehao Dou, et al. ∙

research

∙ 09/23/2018

Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

We improve the robustness of deep neural nets to adversarial attacks by ...

15 Bao Wang, et al. ∙

research

∙ 06/17/2018

Laplacian Smoothing Gradient Descent

We propose a very simple modification of gradient descent and stochastic...

15 Stanley Osher, et al. ∙

research

∙ 05/18/2018

Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning

Deep neural networks (DNNs) typically have enough capacity to fit random...

1 Wei Zhu, et al. ∙

research

∙ 04/02/2018

Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data

We present a generic framework for spatio-temporal (ST) data modeling, a...

1 Bao Wang, et al. ∙

research

∙ 02/01/2018

Deep Learning with Data Dependent Implicit Activation Function

Though deep neural networks (DNNs) achieve remarkable performances in ma...

1 Bao Wang, et al. ∙

research

∙ 11/23/2017

Deep Learning for Real-Time Crime Forecasting and its Ternarization

Real-time crime forecasting is important. However, accurate prediction o...

1 Bao Wang, et al. ∙

research

∙ 07/09/2017

Deep Learning for Real Time Crime Forecasting

Accurate real time crime prediction is a fundamental issue for public sa...

1 Bao Wang, et al. ∙

Bao Wang

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