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Farnaz Safdarian's Résumé

Model cards - https://arxiv.org/abs/1810.03993 Are "short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups... and intersectional groups... that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information." Model cards were motivated by systematic bias in commercial applications that were discovered only after the models were released. To counter that, the authors "advocate for measures of model performance that contain quantitative evaluation results to be broken down by individual cultural, demographic, or phenotypic groups, domain-relevant conditions, and intersectional analysis combining two (or more) groups and conditions." The emphasis on ethical aspects of the measurements is a distinguishing feature of model cards, compared to other proposals to document models.

Datasheets for Datasets - https://arxiv.org/abs/1803.09010 "Document [the dataset] motivation, composition, collection process, recommended uses, and so on. [They] have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted biases in machine learning systems, facilitate greater reproducibility of machine learning results, and help researchers and practitioners select more appropriate datasets for their chosen tasks.'' The motivation behind the proposal was the electronics industry, where every component has a datasheet that describes its operating characteristics and recommended uses. In machine learning, data is the input for model training. Using the wrong dataset, or using a dataset outside of its original intent, or even not understanding well enough the limitations of a dataset, has dire consequences for the model. However, ``[d]espite the importance of data to machine learning, there is no standardized process for documenting machine learning datasets. To address this gap, we propose datasheets for datasets.''

Template paper for the Robotics: Science and Systems Conference. This template was originally published on ShareLaTeX and subsequently moved to Overleaf in November 2019.

Modelo de publicação para o SIRC 2017 - Centro Universitário Franciscano, baseado no Template SBC

Unofficial template for final reports for DBL courses. An attempt was made to write this template in accordance with the writing manual of march 20, 2018 (the latest version as of 2022). Make sure to check if this manual is still up-to-date and if not, change this template accordingly.

Template for the one-minute lighting talking presentation for the São Paulo School of Advanced Science on Learning from Data

Um template simples com as ferramentas pré configuradas e montadas para escrita de um relatório simples.

Formato de entrega de informes Semana 5: Instalación de Sistema Operativo Linux - Distribución Centos Mininal