132 citations · 1.3k across the 52 of their papers we have counts for
8 papers · 1 filter
Domain-informed neural networks for interaction localization within astroparticle experiments
Shixiao Liang, Aaron Higuera, Christina Peters +4
This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) t…
A Guide to Computational Reproducibility in Signal Processing and Machine Learning
Joseph Shenouda, Waheed U. Bajwa
Computational reproducibility is a growing problem that has been extensively studied among computational researchers and within the signal processing and machine learning research…
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis
Arpita Gang, Waheed U. Bajwa
Principal Component Analysis (PCA) is a fundamental data preprocessing tool in the world of machine learning. While PCA is often thought of as a dimensionality reduction method, th…
A hybrid model-based and learning-based approach for classification using limited number of training samples
Alireza Nooraiepour, Waheed U. Bajwa, Narayan B. Mandayam
The fundamental task of classification given a limited number of training data samples is considered for physical systems with known parametric statistical models. The standalone l…
A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression
Batoul Taki, Mohsen Ghassemi, Anand D. Sarwate +1
This paper considers the problem of matrix-variate logistic regression. It derives the fundamental error threshold on estimating low-rank coefficient matrices in the logistic regre…
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation
Arpita Gang, Bingqing Xiang, Waheed U. Bajwa
Principal Subspace Analysis (PSA) -- and its sibling, Principal Component Analysis (PCA) -- is one of the most popular approaches for dimensionality reduction in signal processing…