Search Results for author:
Found 9 papers, 6 papers with code
MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning
In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites.
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
On complex downstream tasks with limited data, such as fluid flow simulations and fluid-structure interactions, we found CoDA-NO to outperform existing methods on the few-shot learning task by over $36\%$.
Truly Scale-Equivariant Deep Nets with Fourier Layers
In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance.
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning
This article aims to provide a comprehensive insight into how data-driven approaches can complement conventional techniques to solve engineering and physics problems, while also noting some of the major pitfalls of machine learning-based approaches.
PaCMO: Partner Dependent Human Motion Generation in Dyadic Human Activity using Neural Operators
In contrast to the concurrent works, which mainly focus on generating the motion of a single actor from the textual description, we generate the motion of one of the actors from the motion of the other participating actor in the action.
AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation
We present, AdaFNIO - Adaptive Fourier Neural Interpolation Operator, a neural operator-based architecture to perform video frame interpolation.
Generative Adversarial Neural Operators
The inputs to the generator are samples of functions from a user-specified probability measure, e. g., Gaussian random field (GRF), and the generator outputs are synthetic data functions.
U-NO: U-shaped Neural Operators
We show that U-NO results in an average of 26% and 44% prediction improvement on Darcy's flow and turbulent Navier-Stokes equations, respectively, over the state of the art.
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
Handwritten character recognition has been the center of research and a benchmark problem in the sector of pattern recognition and artificial intelligence, and it continues to be a challenging research topic.
