13 citations · 30 across the 9 of their papers we have counts for
5 papers · 1 filter
Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation
Nishant Panda, Natalie Klein, Dominic Yang +2
Given two probability densities on related data spaces, we seek a map pushing one density to the other while satisfying application-dependent constraints. For maps to have utility…
Generative structured normalizing flow Gaussian processes applied to spectroscopic data
Natalie Klein, Nishant Panda, Patrick Gasda +1
In this work, we propose a novel generative model for mapping inputs to structured, high-dimensional outputs using structured conditional normalizing flows and Gaussian process reg…
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa +8
Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to…
Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra
Katiana Kontolati, Natalie Klein, Nishant Panda +1
Constructing probability densities for inference in high-dimensional spectral data is often intractable. In this work, we use normalizing flows on structured spectral latent spaces…
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Yinan Wang, Diane Oyen, Weihong +7
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal…