Stanley J. Osher | DeepAI

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Long Chen
127 publications
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73 publications
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48 publications
Jack Xin
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Bao Wang
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∙ 08/09/2023

Prompting In-Context Operator Learning with Sensor Data, Equations, and Natural Language

In the growing domain of scientific machine learning, in-context operato...

0 Liu Yang, et al. ∙

∙ 04/17/2023

In-Context Operator Learning for Differential Equation Problems

This paper introduces a new neural-network-based approach, namely IN-con...

0 Liu Yang, et al. ∙

∙ 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. ∙

∙ 06/30/2022

Multi-Agent Shape Control with Optimal Transport

We introduce a method called MASCOT (Multi-Agent Shape Control with Opti...

0 Alex Tong Lin, et al. ∙

∙ 06/01/2022

Transformer with Fourier Integral Attentions

Multi-head attention empowers the recent success of transformers, the st...

79 Tan Nguyen, et al. ∙

∙ 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. ∙

∙ 10/16/2021

Transformer with a Mixture of Gaussian Keys

Multi-head attention is a driving force behind state-of-the-art transfor...

9 Tam Nguyen, et al. ∙

∙ 10/10/2021

Heavy Ball Neural Ordinary Differential Equations

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

15 Hedi Xia, et al. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 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. ∙

∙ 07/26/2012

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Given a graph where vertices represent alternatives and arcs represent p...

0 Braxton Osting, et al. ∙

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