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Found 17 papers, 6 papers with code
RRCNN$^{+}$: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition
Time-frequency analysis is an important and challenging task in many applications.
RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network
Inspired by the successful applications of deep learning in fields like image processing and natural language processing, and given the lack in the literature of works in which deep learning techniques are used directly to decompose non-stationary signals into simple oscillatory components, we use the convolutional neural network, residual structure and nonlinear activation function to compute in an innovative way the local average of the signal, and study a new non-stationary signal decomposition method under the framework of deep learning.
Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study
In this work, a comprehensive numerical study involving analysis and experiments shows why a two-layer neural network has difficulties handling high frequencies in approximation and learning when machine precision and computation cost are important factors in real practice.
Neural Control of Parametric Solutions for High-dimensional Evolution PDEs
Numerical experiments on different high-dimensional evolution PDEs with various initial conditions demonstrate the promising results of the proposed method.
Discrete Langevin Sampler via Wasserstein Gradient Flow
It is known that gradient-based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL divergence on a Wasserstein manifold.
Neural Monge Map estimation and its applications
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another.
Mean Field Game GAN
We propose a novel mean field games (MFGs) based GAN(generative adversarial network) framework.
Learning High Dimensional Wasserstein Geodesics
We propose a new formulation and learning strategy for computing the Wasserstein geodesic between two probability distributions in high dimensions.
Time-frequency representation of nonstationary signals: the IMFogram
Iterative filtering methods were introduced around 2010 to improve definitions and measurements of structural features in signal processing.
Numerical Analysis Numerical Analysis
Numerical Solution of Inverse Problems by Weak Adversarial Networks
We consider a weak adversarial network approach to numerically solve a class of inverse problems, including electrical impedance tomography and dynamic electrical impedance tomography problems.
Learning Cost Functions for Optimal Transport
Inverse optimal transport (OT) refers to the problem of learning the cost function for OT from observed transport plan or its samples.
Learning Stochastic Behaviour from Aggregate Data
Learning nonlinear dynamics from aggregate data is a challenging problem because the full trajectory of each individual is not available, namely, the individual observed at one time may not be observed at the next time point, or the identity of individual is unavailable.
Weak Adversarial Networks for High-dimensional Partial Differential Equations
The weak solution and the test function in the weak formulation are then parameterized as the primal and adversarial networks respectively, which are alternately updated to approximate the optimal network parameter setting.
Numerical Analysis Numerical Analysis
Learning to Match via Inverse Optimal Transport
We emphasize that the discrete optimal transport plays the role of a variational principle which gives rise to an optimization-based framework for modeling the observed empirical matching data.
Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT
Real life signals are in general non--stationary and non--linear.
Numerical Analysis
Hyperspectral Chemical Plume Detection Algorithms Based On Multidimensional Iterative Filtering Decomposition
Hyperspectral images can be used to identify chemical plumes, however the task can be extremely challenging.
Adaptive Local Iterative Filtering for Signal Decomposition and Instantaneous Frequency analysis
We provide sufficient conditions on the filters that ensure the convergence of IFs applied to any $L^2$ signal.
Numerical Analysis
