Demo Data from EDBT 2026 / reviewers in the wild / expert
Shashaank Vattikuti
dblp:215/2646
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
interpretability |
0.5 | 1 | 2021 | Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization · ICLR 2021 |
Machine learning › Representation and self-supervised learning
matrix factorization |
0.5 | 1 | 2021 | 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 |
0.5 | 1 | 2021 | 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 |
0.5 | 1 | 2021 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
ICLR | 5 |
| 2016 | Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry MemoryabstractIt 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 |