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Aidana Karipbayeva

dblp:249/5611 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Artificial intelligence
1 paper
Representation and self-supervised learning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › text embedding
sentence embedding		 0.412020
A Critique of the Smooth Inverse Frequency Sentence Embeddings (Student Abstract) · AAAI 2020
Information retrieval › text analysis › text representation
sentence embedding		 0.412020
A Critique of the Smooth Inverse Frequency Sentence Embeddings (Student Abstract) · AAAI 2020
Information retrieval › text analysis
text representation		 0.412020
A Critique of the Smooth Inverse Frequency Sentence Embeddings (Student Abstract) · AAAI 2020

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

smooth inverse frequency · 0.9
YearPublicationVenuePosition
2020 A Critique of the Smooth Inverse Frequency Sentence Embeddings (Student Abstract)
abstract
We critically review the smooth inverse frequency sentence embedding method of Arora, Liang, and Ma (2017), and show inconsistencies in its setup, derivation and evaluation.
Aidana Karipbayeva, Alena Sorokina, Zhenisbek Assylbekov
 AAAI1
2019 Low-Rank Approximation of Matrices for PMI-Based Word Embeddings
Alena Sorokina, Aidana Karipbayeva, Zhenisbek Assylbekov
 CICLing (1)2