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1
Optimizing a portfolio of agri-environmental investments ...
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2
“Institution” and “institute”: tendency to the “false substitution” of concepts ...
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3
Delving Deeper into Cross-lingual Visual Question Answering ...
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4
Testing the interface hypothesis: the acquisition of English indirect questions by L1 speakers of Omani Arabic
In: Heliyon (2022)
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5
Geschichte der Brille : Verfahren der Herstellung von Brillenfassungen aus Celluloseacetat : Terminologiearbeit im Sprachpaar Italienisch – Deutsch, Deutsch – Italienisch
Kloster, Iryna [Verfasser]. - Mainz : Universitätsbibliothek der Johannes Gutenberg-Universität Mainz, 2021
DNB Subject Category Language
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6
Combating Temporal Drift in Crisis with Adapted Embeddings ...
Stowe, Kevin; Gurevych, Iryna. - : arXiv, 2021
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7
The concept of security in the integration processes of the European union countries ...
Grushko, Iryna. - : Zenodo, 2021
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8
The concept of security in the integration processes of the European union countries ...
Grushko, Iryna. - : Zenodo, 2021
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9
Ideas Challenge 2020/2021 : WikiData Integration with Repository Contents ...
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10
Ideas Challenge 2020/2021 : WikiData Integration with Repository Contents ...
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11
Annotation Curricula to Implicitly Train Non-Expert Annotators ...
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12
Lesia Ukrainka in the Global Information Space: Case-Study of the Audio Guide Creation for the Lesia Ukrainka Museum ...
Biskub, Iryna; Anna, Danylchuk.. - : Zenodo, 2021
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13
Lesia Ukrainka in the Global Information Space: Case-Study of the Audio Guide Creation for the Lesia Ukrainka Museum ...
Biskub, Iryna; Anna, Danylchuk.. - : Zenodo, 2021
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14
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ...
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15
xGQA: Cross-Lingual Visual Question Answering ...
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16
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ...
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17
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.77 Abstract: Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no previous work investigated how dense representations perform with large index sizes. We show theoretically and empirically that the performance for dense representations decreases quicker than sparse representations for increasing index sizes. In extreme cases, this can even lead to a tipping point where at a certain index size sparse representations outperform dense representations. We show that this behavior is tightly connected to the number of dimensions of the representations: The lower the dimension, the higher the chance for false positives, i.e. returning irrelevant documents ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/z3am-4347
https://underline.io/lecture/25657-the-curse-of-dense-low-dimensional-information-retrieval-for-large-index-sizes
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18
Green NLP panel ...
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19
Investigating label suggestions for opinion mining in German Covid-19 social media ...
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20
Coreference Reasoning in Machine Reading Comprehension ...
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