Nico Hoffmann | DeepAI

DeepAI AI Chat

Featured Co-authors

Stefan Gumhold
17 publications
Nishant Kumar
17 publications
Michael Bussmann
15 publications
Yueyang Teng
14 publications
Yen-Yu Chang
11 publications
Michael Hecht
9 publications
Alexander Debus
8 publications
Jens Müller
7 publications
Levin Kuhlmann
7 publications
Richard Pausch
6 publications
Maksim Zhdanov
5 publications

∙ 09/15/2023

Ensuring Toplogical Data-Structure Preservation under Autoencoder Compression due to Latent Space Regularization in Gauss–Legendre nodes

We formulate a data independent latent space regularisation constraint f...

0 Chethan Krishnamurthy Ramanaik, et al. ∙

∙ 02/27/2023

Learning Electron Bunch Distribution along a FEL Beamline by Normalising Flows

Understanding and control of Laser-driven Free Electron Lasers remain to...

0 Anna Willmann, et al. ∙

∙ 12/16/2022

LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery

Modelling the temperature of Electric Vehicle (EV) batteries is a fundam...

0 Jeyhun Rustamov, et al. ∙

∙ 12/12/2022

Implicit Neural Convolutional Kernels for Steerable CNNs

Steerable convolutional neural networks (CNNs) provide a general framewo...

0 Maksim Zhdanov, et al. ∙

∙ 12/12/2022

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 nea...

0 Thomas Miethlinger, et al. ∙

∙ 11/09/2022

Continual learning autoencoder training for a particle-in-cell simulation via streaming

The upcoming exascale era will provide a new generation of physics simul...

0 Patrick Stiller, et al. ∙

∙ 10/04/2022

Amortized Bayesian Inference of GISAXS Data with Normalizing Flows

Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imag...

0 Maksim Zhdanov, et al. ∙

∙ 06/04/2022

Learning Generative Factors of Neuroimaging Data with Variational auto-encoders

Neuroimaging techniques produce high-dimensional, stochastic data from w...

0 Maksim Zhdanov, et al. ∙

∙ 06/04/2022

Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer

Functional connectivity plays an essential role in modern neuroscience. ...

0 Maksim Zhdanov, et al. ∙

∙ 12/10/2021

Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

A variety of attention mechanisms have been studied to improve the perfo...

0 Yichao Liu, et al. ∙

∙ 11/24/2021

NAM: Normalization-based Attention Module

Recognizing less salient features is the key for model compression. Howe...

0 Yichao Liu, et al. ∙

∙ 06/01/2021

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 pr...

0 Friedrich Bethke, et al. ∙

∙ 06/01/2021

Data-Driven Shadowgraph Simulation of a 3D Object

In this work we propose a deep neural network based surrogate model for ...

0 Anna Willmann, et al. ∙

∙ 09/08/2020

Large-scale Neural Solvers for Partial Differential Equations

Solving partial differential equations (PDE) is an indispensable part of...

0 Patrick Stiller, et al. ∙

∙ 01/26/2020

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 v...

0 Nishant Kumar, et al. ∙

∙ 08/11/2019

Structural Similarity based Anatomical and Functional Brain Imaging Fusion

Multimodal medical image fusion helps in combining contrasting features ...

1 Nishant Kumar, et al. ∙

∙ 11/02/2018

Convolutional Neural Networks for Epileptic Seizure Prediction

Epilepsy is the most common neurological disorder and an accurate foreca...

0 Matthias Eberlein, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world