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Found 11 papers, 4 papers with code
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
First, symbolic regression is used to identify optimal formulas that fit input data by utilizing base functions such as logarithmic, trigonometric, and exponential functions.
Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps
Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic.
Wave simulation in non-smooth media by PINN with quadratic neural network and PML condition
We first illustrate that non-smooth velocity models lead to inaccurate wavefields when no boundary conditions are implemented in the loss function.
Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning
Seismic full waveform inversion (FWI) is a powerful geophysical imaging technique that produces high-resolution subsurface models by iteratively minimizing the misfit between the simulated and observed seismograms.
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
Our work paves the way to reliable phase imaging of thick and complex samples with QPI.
Data-driven geophysics: from dictionary learning to deep learning
In this article, we review the basic concepts of and recent advances in data-driven approaches from dictionary learning to deep learning in a variety of geophysical scenarios.
Can learning from natural image denoising be used for seismic data interpolation?
We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation.
Deep-learning inversion: a next generation seismic velocity-model building method
Seismic velocity is one of the most important parameters used in seismic exploration.
Deep learning for denoising
We use a convolutional neural network as the basic tool for deep learning.
Hartley Spectral Pooling for Deep Learning
In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size.
Enhanced image approximation using shifted rank-1 reconstruction
In this work we introduce a generalization using shifted rank-1 matrices to approximate $A\in\mathbb{C}^{M\times N}$.
Numerical Analysis Numerical Analysis
