77 citations · 410 across the 26 of their papers we have counts for
27 papers
An improved standardization framework for Type II-P supernovae using a hierarchical Bayesian approach
A. Holas, S. Taubenberger, C. Vogl +41
We use Type II-P supernovae (SNe II-P) observed as part of the Nearby Supernova Factory (SNfactory) experiment as standardizable candles to develop an improved standardization fram…
Training Multilayer Perceptrons by Sampling with Quantum Annealers
Frances Fengyi Yang, Michele Sasdelli, Tat-Jun Chin
A successful application of quantum annealing to machine learning is training restricted Boltzmann machines (RBM). However, many neural networks for vision applications are feedfor…
Mutual information neural estimation for unsupervised multi-modal registration of brain images
Gerard Snaauw, Michele Sasdelli, Gabriel Maicas +4
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based regis…
A Hybrid Quantum-Classical Algorithm for Robust Fitting
Anh-Dzung Doan, Michele Sasdelli, David Suter +1
Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do n…
Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector
Andrew Du, Yee Wei Law, Michele Sasdelli +4
Data collected by Earth-observing (EO) satellites are often afflicted by cloud cover. Detecting the presence of clouds -- which is increasingly done using deep learning -- is cruci…
Physical Adversarial Attacks on an Aerial Imagery Object Detector
Andrew Du, Bo Chen, Tat-Jun Chin +4
Deep neural networks (DNNs) have become essential for processing the vast amounts of aerial imagery collected using earth-observing satellite platforms. However, DNNs are vulnerabl…