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Found 19 papers, 8 papers with code
Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
In this paper, we introduce a new finite expression method (FEX) to solve high-dimensional partial integro-differential equations (PIDEs).
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs).
Probing reaction channels via reinforcement learning
We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths.
A Generic Shared Attention Mechanism for Various Backbone Neural Networks
The self-attention mechanism has emerged as a critical component for improving the performance of various backbone neural networks.
On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver
The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines.
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network
Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
Finite Expression Method for Solving High-Dimensional Partial Differential Equations
Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topic in computational science and engineering, mainly due to the "curse of dimensionality" in designing numerical schemes that scale in dimension.
Deep Reinforcement Learning
Vocal Bursts Intensity Prediction
Stiffness-aware neural network for learning Hamiltonian systems
We propose stiffness-aware neural network (SANN), a new method for learning Hamiltonian dynamical systems from data.
Stationary Density Estimation of Itô Diffusions Using Deep Learning
In this paper, we consider the density estimation problem associated with the stationary measure of ergodic It\^o diffusions from a discrete-time series that approximate the solutions of the stochastic differential equations.
AlterSGD: Finding Flat Minima for Continual Learning by Alternative Training
Deep neural networks suffer from catastrophic forgetting when learning multiple knowledge sequentially, and a growing number of approaches have been proposed to mitigate this problem.
Blending Pruning Criteria for Convolutional Neural Networks
One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive "importance".
Solving PDEs on Unknown Manifolds with Machine Learning
In a well-posed elliptic PDE setting, when the hypothesis space consists of neural networks with either infinite width or depth, we show that the global minimizer of the empirical loss function is a consistent solution in the limit of large training data.
Reproducing Activation Function for Deep Learning
We propose reproducing activation functions (RAFs) to improve deep learning accuracy for various applications ranging from computer vision to scientific computing.
Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks
We apply the method to 11, 790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities.
Efficient Attention Network: Accelerate Attention by Searching Where to Plug
Recently, many plug-and-play self-attention modules are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural networks (CNNs).
Machine Learning for Prediction with Missing Dynamics
This article presents a general framework for recovering missing dynamical systems using available data and machine learning techniques.
Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise
Batch Normalization (BN)(Ioffe and Szegedy 2015) normalizes the features of an input image via statistics of a batch of images and hence BN will bring the noise to the gradient of the training loss.
DIANet: Dense-and-Implicit Attention Network
Attention networks have successfully boosted the performance in various vision problems.
Ranked #142 on
Image Classification
on CIFAR-100
(using extra training data)
Drop-Activation: Implicit Parameter Reduction and Harmonic Regularization
Overfitting frequently occurs in deep learning.
Ranked #9 on
Image Classification
on SVHN
