49 citations · 66 across the 6 of their papers we have counts for
6 papers
LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators
Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert
Modeling high-frequency information is a critical challenge in scientific machine learning. For instance, fully turbulent flow simulations of the Navier-Stokes equations at Reynold…
Active Learning for Neural PDE Solvers
Daniel Musekamp, Marimuthu Kalimuthu, David Holzmüller +2
Solving partial differential equations (PDEs) is a fundamental problem in science and engineering. While neural PDE solvers can be more efficient than established numerical solvers…
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp +1
Transformer models are increasingly used for solving Partial Differential Equations (PDEs). Several adaptations have been proposed, all of which suffer from the typical problems of…
Fusion Models for Improved Visual Captioning
Marimuthu Kalimuthu, Aditya Mogadala, Marius Mosbach +1
Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image captioning models are trained on human annotated datasets such as Flickr30k and…
A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task
Marimuthu Kalimuthu, Fabrizio Nunnari, Daniel Sonntag
The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts. We describe our Deep Neural Network (DNN) bas…
Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods
Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow
Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in…