Biological data annotation via a human-augmenting AI-based labeling system

Autor: Douwe van der Wal, Iny Jhun, Israa Laklouk, Jeff Nirschl, Lara Richer, Rebecca Rojansky, Talent Theparee, Joshua Wheeler, Jörg Sander, Felix Feng, Osama Mohamad, Silvio Savarese, Richard Socher, Andre Esteva

Publikováno v: npj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)

Abstract Biology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, lab

Externí odkaz: https://doaj.org/article/d6164252a0d94935879eacf682d4bdaf

Schlankes und elegantes integrales Brückenbauwerk in Leipzig – die neue Messebrücke

Autor: Michael Reichel, Rüdiger Langkabel, Jörg Sander

Publikováno v: Beton- und Stahlbetonbau. 117:733-743

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6fba05a11b6d69cd52c76016d1ac261a
https://doi.org/10.1002/best.202200070

Efficient Computation and Visualization of Multiple Density-Based Clustering Hierarchies

Autor: Jörg Sander, Mario A. Nascimento, Ricardo J. G. B. Campello, Antonio Cavalcante Araujo Neto

Publikováno v: IEEE Transactions on Knowledge and Data Engineering. 33:3075-3089

HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter $mpts$ m p t s . While a small change in $mpts$ m p t s typically leads to a small change in t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::33bc92b86b16e9ba481c1e389341ea83
https://doi.org/10.1109/tkde.2019.2962412

Internal Evaluation of Unsupervised Outlier Detection

Autor: Ricardo J. G. B. Campello, Jörg Sander, Arthur Zimek, Henrique O. Marques

Publikováno v: Marques, H O, Campello, R J G B, Sander, J & Zimek, A 2020, ' Internal Evaluation of Unsupervised Outlier Detection ', ACM Transactions on Knowledge Discovery from Data, vol. 14, no. 4, 47 . https://doi.org/10.1145/3394053
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, base

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0b82752d0a1ba71b9c881fc08ef5544
https://doi.org/10.1145/3394053

A unified view of density-based methods for semi-supervised clustering and classification

Autor: Jörg Sander, Jadson Castro Gertrudes, Ricardo J. G. B. Campello, Arthur Zimek

Publikováno v: Data Mining and Knowledge Discovery
Castro Gertrudes, J, Zimek, A, Sander, J & Campello, R J G B 2019, ' A unified view of density-based methods for semi-supervised clustering and classification ', Data Mining and Knowledge Discovery, vol. 33, no. 6, pp. 1894-1952 . https://doi.org/10.1007/s10618-019-00651-1
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP

Semi-supervised learning is drawing increasing attention in the era of big data, as the gap between the abundance of cheap, automatically collected unlabeled data and the scarcity of labeled data that are laborious and expensive to obtain is dramatic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86bcbb12a5573963639c5d0160725d9e
https://doi.org/10.1007/s10618-019-00651-1