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Found 13 papers, 3 papers with code
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
Motion correction in MRI using deep learning and a novel hybrid loss function
Evaluation used simulated T1 and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans.
VQ-Flows: Vector Quantized Local Normalizing Flows
Normalizing flows provide an elegant approach to generative modeling that allows for efficient sampling and exact density evaluation of unknown data distributions.
Permutation Invariant Representations with Applications to Graph Deep Learning
In turn, this proves that almost any classifier can be implemented with an arbitrary small loss of performance.
Convergence Guarantees for Deep Epsilon Greedy Policy Learning
We show an error or regret bound and convergence of the Deep Epsilon Greedy method which chooses actions with a neural network's prediction.
Non-Linear Operator Approximations for Initial Value Problems
The Padé exponential operator uses a $\textit{recurrent structure with shared parameters}$ to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession.
An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans
Finding an optimal correspondence between point sets is a common task in computer vision.
On Lipschitz Bounds of General Convolutional Neural Networks
Many convolutional neural networks (CNNs) have a feed-forward structure.
Learning flexible representations of stochastic processes on graphs
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations.
Lipschitz Properties for Deep Convolutional Networks
In this paper we discuss the stability properties of convolutional neural networks.
Phase Retrieval using Lipschitz Continuous Maps
In this note we prove that reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem") can be performed using Lipschitz continuous maps.
Stability of Phase Retrievable Frames
In this paper we study the property of phase retrievability by redundant sysems of vectors under perturbations of the frame set.
Invertibility and Robustness of Phaseless Reconstruction
This paper is concerned with the question of reconstructing a vector in a finite-dimensional real Hilbert space when only the magnitudes of the coefficients of the vector under a redundant linear map are known.
