From the 1 of 6 linked papers with an AI index.
6 papers
Bayesian Graphical Models under Positivity Constraints: A Scalable generalized likelihood Approach
Swarnali Raha, Partha Sarkar, Sirani Perera +1
The paper proposes a scalable Bayesian method for estimating precision matrices in Gaussian graphical models with total positivity constraints, using a D‑trace loss and spike‑and‑s…
Fully Convolutional Spatiotemporal Learning for Microstructure Evolution Prediction
Michael Trimboli, Mohammed Alsubaie, Sirani M. Perera +2
Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulati…
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Mohammed Alsubaie, Wenxi Liu, Linxia Gu +3
Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive cl…
A Low-complexity Structured Neural Network to Realize States of Dynamical Systems
Hansaka Aluvihare, Levi Lingsch, Xianqi Li +1
Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differe…
A Low-complexity Structured Neural Network Approach to Intelligently Realize Wideband Multi-beam Beamformers
Hansaka Aluvihare, Sivakumar Sivasankar, Xianqi Li +2
True-time-delay (TTD) beamformers can produce wideband, squint-free beams in both analog and digital signal domains, unlike frequency-dependent FFT beams. Our previous work showed…
Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains
Levi Lingsch, Mike Y. Michelis, Emmanuel de Bezenac +3
The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations.…