Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Heiter, Edith"'
Autor:
Buyl, Maarten, Rogiers, Alexander, Noels, Sander, Dominguez-Catena, Iris, Heiter, Edith, Romero, Raphael, Johary, Iman, Mara, Alexandru-Cristian, Lijffijt, Jefrey, De Bie, Tijl
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants
Externí odkaz:
http://arxiv.org/abs/2410.18417
Autor:
Heiter, Edith, Martens, Liesbet, Seurinck, Ruth, Guilliams, Martin, De Bie, Tijl, Saeys, Yvan, Lijffijt, Jefrey
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, but insights
Externí odkaz:
http://arxiv.org/abs/2406.12953
Publikováno v:
Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond label information. This is useful, for example, when one wants to fact
Externí odkaz:
http://arxiv.org/abs/2302.03493
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or t
Externí odkaz:
http://arxiv.org/abs/2301.03338
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support users in interpreting apparent cluster structure on scatter plots where
Externí odkaz:
http://arxiv.org/abs/2111.03168
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they are widely used, they visualize data as is, rather than in light of the
Externí odkaz:
http://arxiv.org/abs/2103.01828
We formally verify several computational reductions concerning the Post correspondence problem (PCP) using the proof assistant Coq. Our verifications include a reduction of a string rewriting problem generalising the halting problem for Turing machin
Externí odkaz:
http://arxiv.org/abs/1711.07023
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topological knowledge about the data, such as a known cluster structure or t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c330718172975c821e0362cb99ed3483
http://arxiv.org/abs/2110.09193
http://arxiv.org/abs/2110.09193