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Found 9 papers, 5 papers with code
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning
But randomly sampling data to be annotated is often inefficient as it ignores the characteristics of the data and the specific needs of the model.
Augmenting a Physics-Informed Neural Network for the 2D Burgers Equation by Addition of Solution Data Points
Finally, we argue that the addition of the governing equations during training may provide a way to improve the overall performance of the model without relying on additional data, which is especially important for situations where the number of known solution points is limited.
Reinforcement Learning Applied to Trading Systems: A Survey
The recent achievements and the consequent notoriety of Reinforcement Learning (RL) have also increased its adoption in trading tasks.
Tracking environmental policy changes in the Brazilian Federal Official Gazette
Even though most of its energy generation comes from renewable sources, Brazil is one of the largest emitters of greenhouse gases in the world, due to intense farming and deforestation of biomes such as the Amazon Rainforest, whose preservation is essential for compliance with the Paris Agreement.
ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling
Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages.
DEBACER: a method for slicing moderated debates
In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal.
PLSUM: Generating PT-BR Wikipedia by Summarizing Multiple Websites
In an effort to expand the Brazilian Wikipedia, we contribute PLSum, a framework for generating wiki-like abstractive summaries from multiple descriptive websites.
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach
The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers.
DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment
The challenge of climate change and biome conservation is one of the most pressing issues of our time - particularly in Brazil, where key environmental reserves are located.
