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 SIGIR 2026 / reviewers in the wild / expert

Mira Welner

dblp:324/1711 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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
Learning paradigms · 50% Deep learning architectures and training · 50%
Computer graphics and multimedia
1 paper
Image and video processing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training
autoencoder		 0.612022
Unsupervised Identification of Materials with Hyperspectral Images · AAAI 2022
Machine learning › Learning paradigms
unsupervised learning		 0.612022
Unsupervised Identification of Materials with Hyperspectral Images · AAAI 2022
Image and video processing
hyperspectral image analysis		 0.612022
Unsupervised Identification of Materials with Hyperspectral Images · AAAI 2022
Image and video processing › hyperspectral image analysis
material identification		 0.612022
Unsupervised Identification of Materials with Hyperspectral Images · AAAI 2022

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

modified autoencoder · 1.1
YearPublicationVenuePosition
2022 Unsupervised Identification of Materials with Hyperspectral Images
abstract
We introduce a novel technique to identify three spectra representing the three primary materials in a hyperspectral image of a scene. We accomplish this using a modified autoencoder. Further research will be conducted to verify the accuracy of these spectra.
Mira Welner
 AAAI1