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Found 18 papers, 11 papers with code
Ensuring Topological Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss--Legendre nodes
Revisiting this classic enables to prove that regularised autoencoders ensure a one-to-one re-embedding of the initial data manifold to its latent representation.
Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows
Understanding and control of Laser-driven Free Electron Lasers remain to be difficult problems that require highly intensive experimental and theoretical research.
LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery
The results showed that LiFe-net trained with time stability loss outperforms the other two models and can estimate the temperature evolution on unseen data with a relative error of 2. 77 % on average.
Implicit Convolutional Kernels for Steerable CNNs
Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and transformations of an origin-preserving group $G$, such as reflections and rotations.
Acceptance Rates of Invertible Neural Networks on Electron Spectra from Near-Critical Laser-Plasmas: A Comparison
While the interaction of ultra-intense ultra-short laser pulses with near- and overcritical plasmas cannot be directly observed, experimentally accessible quantities (observables) often only indirectly give information about the underlying plasma dynamics.
Continual learning autoencoder training for a particle-in-cell simulation via streaming
These simulations will have a high spatiotemporal resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible.
Amortized Bayesian Inference of GISAXS Data with Normalizing Flows
Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials.
Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer
One such pathology is schizophrenia which is often followed by auditory verbal hallucinations.
Learning Generative Factors of EEG Data with Variational auto-encoders
Electroencephalography produces high-dimensional, stochastic data from which it might be challenging to extract high-level knowledge about the phenomena of interest.
Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions
A variety of attention mechanisms have been studied to improve the performance of various computer vision tasks.
NAM: Normalization-based Attention Module
Recognizing less salient features is the key for model compression.
InFlow: Robust outlier detection utilizing Normalizing Flows
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
Data-Driven Shadowgraph Simulation of a 3D Object
In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium.
Invertible Surrogate Models: Joint surrogate modelling and reconstruction of Laser-Wakefield Acceleration by invertible neural networks
Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode.
Large-scale Neural Solvers for Partial Differential Equations
However, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing.
Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas
The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation.
Structural Similarity based Anatomical and Functional Brain Imaging Fusion
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image.
Convolutional Neural Networks for Epileptic Seizure Prediction
In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction.
