2 citations · 2 across the 2 of their papers we have counts for
6 papers
Inversion of Magnetic Data using Learned Dictionaries and Scale Space
Shadab Ahamed, Simon Ghyselincks, Pablo Chang Huang Arias +5
Magnetic data inversion is an important tool in geophysics, used to infer subsurface magnetic susceptibility distributions from surface magnetic field measurements. This inverse pr…
Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling
Eldad Haber, Shadab Ahamed, Md. Shahriar Rahim Siddiqui +2
Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for entertainment to solving inverse problems.…
Multiscale Training of Convolutional Neural Networks
Shadab Ahamed, Niloufar Zakariaei, Eldad Haber +1
Training convolutional neural networks (CNNs) on high-resolution images is often bottlenecked by the cost of evaluating gradients of the loss on the finest spatial mesh. To address…
Advection Augmented Convolutional Neural Networks
Niloufar Zakariaei, Siddharth Rout, Eldad Haber +1
Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation a…
Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging
Niloufar Zakariaei, Arman Rahmim, Eldad Haber
Dynamic Positron Emission Tomography (dPET) imaging and Time-Activity Curve (TAC) analyses are essential for understanding and quantifying the biodistribution of radiopharmaceutica…
MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipeline
Yasamin Medghalchi, Niloufar Zakariaei, Arman Rahmim +1
The effectiveness of Deep Neural Networks (DNNs) heavily relies on the abundance and accuracy of available training data. However, collecting and annotating data on a large scale i…