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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...
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04/17/2023
In-Context Operator Learning for Differential Equation Problems
This paper introduces a new neural-network-based approach, namely IN-con...
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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...
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06/30/2022
Multi-Agent Shape Control with Optimal Transport
We introduce a method called MASCOT (Multi-Agent Shape Control with Opti...
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06/01/2022
Transformer with Fourier Integral Attentions
Multi-head attention empowers the recent success of transformers, the st...
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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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10/16/2021
Transformer with a Mixture of Gaussian Keys
Multi-head attention is a driving force behind state-of-the-art transfor...
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10/10/2021
Heavy Ball Neural Ordinary Differential Equations
We propose heavy ball neural ordinary differential equations (HBNODEs), ...
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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...
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06/12/2020
MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
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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 ...
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02/24/2020
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Stochastic gradient descent (SGD) with constant momentum and its variant...
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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...
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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...
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01/21/2019
A Deterministic Approach to Avoid Saddle Points
Loss functions with a large number of saddle points are one of the main ...
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11/26/2018
EnResNet: ResNet Ensemble via the Feynman-Kac Formalism
We propose a simple yet powerful ResNet ensemble algorithm which consist...
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11/15/2018
Mathematical Analysis of Adversarial Attacks
In this paper, we analyze efficacy of the fast gradient sign method (FGS...
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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 ...
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02/01/2018
Deep Learning with Data Dependent Implicit Activation Function
Though deep neural networks (DNNs) achieve remarkable performances in ma...
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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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07/26/2012