Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Alice Stanboli

dblp:218/6964 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Image recognition and object detection · 77% Transfer learning and domain adaptation · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › image classification
content-based image classification		 0.312018
Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas · AAAI 2018
Machine learning › Transfer learning and domain adaptation
cross-domain learning		 0.112018
Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas · AAAI 2018

Methods — techniques the papers use, named apart from their topics

transfer learning · 0.3convolutional neural network · 0.3
YearPublicationVenuePosition
2018 Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas
abstract
NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.
Kiri Wagstaff, Alice Stanboli, Kevin Grimes, Thamme Gowda, Jordan Padams
 AAAI3