Search Results for author: Aécio Santos

Found 5 papers, 1 papers with code

Correlation Sketches for Approximate Join-Correlation Queries

no code implementations7 Apr 2021 Aécio Santos, Aline Bessa, Fernando Chirigati, Christopher Musco, Juliana Freire

The increasing availability of structured datasets, from Web tables and open-data portals to enterprise data, opens up opportunities~to enrich analytics and improve machine learning models through relational data augmentation.

Data Augmentation

Auctus: A Dataset Search Engine for Data Augmentation

no code implementations10 Feb 2021 Sonia Castelo, Rémi Rampin, Aécio Santos, Aline Bessa, Fernando Chirigati, Juliana Freire

The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions.

Data Augmentation

Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

no code implementations5 Jul 2019 Aécio Santos, Sonia Castelo, Cristian Felix, Jorge Piazentin Ono, Bowen Yu, Sungsoo Hong, Cláudio T. Silva, Enrico Bertini, Juliana Freire

In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.

AutoML BIG-bench Machine Learning

A Topic-Agnostic Approach for Identifying Fake News Pages

1 code implementation2 May 2019 Sonia Castelo, Thais Almeida, Anas Elghafari, Aécio Santos, Kien Pham, Eduardo Nakamura, Juliana Freire

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes.

Misinformation TAG

Bootstrapping Domain-Specific Content Discovery on the Web

no code implementations25 Feb 2019 Kien Pham, Aécio Santos, Juliana Freire

Given a domain of interest $D$, subject-matter experts (SMEs) must search for relevant websites and collect a set of representative Web pages to serve as training examples for creating a classifier that recognizes pages in $D$, as well as a set of pages to seed the crawl.

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