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

Shashaank Vattikuti

dblp:215/2646 · DBLP profile ↗
← Back
2ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-2109-3512ORCID · reported

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

Artificial intelligence and machine learning · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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
Representation and self-supervised learning · 75% Trustworthy machine learning · 25%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Trustworthy machine learning
interpretability
0.512021
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization · ICLR 2021
Machine learning › Representation and self-supervised learning
matrix factorization
0.512021
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization · ICLR 2021
Machine learning › Representation and self-supervised learning › matrix factorization
probabilistic matrix factorization
0.512021
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization · ICLR 2021
Machine learning › Representation and self-supervised learning › representation learning › unsupervised representation learning
sparse coding
0.512021
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization · ICLR 2021

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

sparse encoding · 0.5
YearPublicationVenuePosition
2021 Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
Joshua C. Chang, Patrick Fletcher, Jungmin Han, Ted L. Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson C. Chow
ICLR5
2016 Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory
abstract
It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.
Shashaank Vattikuti, Phyllis Thangaraj, Hua W. Xie, Stephen J. Gotts, Carson C. Chow
PLoS Comput. Biol.1