Zobrazeno 1 - 10
of 7 924
pro vyhledávání: '"Neto Francisco"'
This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-
Externí odkaz:
http://arxiv.org/abs/2409.06593
This research aims to propose and evaluate a novel model named K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART) for improved estimation of Average Treatment Effects (ATE) and Conditional Average Treatment Effects (CATE). The stud
Externí odkaz:
http://arxiv.org/abs/2409.05665
This paper critically examines current methodologies for evaluating models in Conditional and Average Treatment Effect (CATE/ATE) estimation, identifying several key pitfalls in existing practices. The current approach of over-reliance on specific me
Externí odkaz:
http://arxiv.org/abs/2409.05161
The increasing demand for software engineering education presents learning challenges in courses due to the diverse range of topics that require practical applications, such as programming or software design, all of which are supported by group work
Externí odkaz:
http://arxiv.org/abs/2407.10322
The integration of Large Language Models (LLMs) and chatbots introduces new challenges and opportunities for decision-making in software testing. Decision-making relies on a variety of information, including code, requirements specifications, and oth
Externí odkaz:
http://arxiv.org/abs/2406.11339
Software developers use natural language to interact not only with other humans, but increasingly also with chatbots. These interactions have different properties and flow differently based on what goal the developer wants to achieve and who they int
Externí odkaz:
http://arxiv.org/abs/2405.12712
Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a
Externí odkaz:
http://arxiv.org/abs/2405.03824
Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little emp
Externí odkaz:
http://arxiv.org/abs/2404.14901
Autor:
Maia, Wesley Ferreira, Carmignani, Angelo, Bortoli, Gabriel, Maretti, Lucas, Luz, David, Guzman, Daniel Camilo Fuentes, Henriques, Marcos Jardel, Neto, Francisco Louzada
This article investigates applying advanced machine learning models, specifically LSTM and BERT, for text classification to predict multiple categories in the retail sector. The study demonstrates how applying data augmentation techniques and the foc
Externí odkaz:
http://arxiv.org/abs/2403.01638
Nanoinformatics is a novel, rapidly growing area of research that involves the application of computational techniques to several aspects of research in the field of nanotechnology, especially concerned its application to biotechnology. This reviews
Externí odkaz:
http://arxiv.org/abs/2401.10365