843 citations · 1k across the 7 of their papers we have counts for
7 papers
Machine learning unveils composition-property relationships in chalcogenide glasses
Saulo M. Mastelini, Daniel R. Cassar, Edesio Alcobaça +3
Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the r…
MeLIME: Meaningful Local Explanation for Machine Learning Models
Tiago Botari, Frederik Hvilshøj, Rafael Izbicki +1
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine…
Predicting and interpreting oxide glass properties by machine learning using large datasets
Daniel R. Cassar, Saulo Martiello Mastelini, Tiago Botari +3
With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly…
Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature
Tanjin He, Wenhao Sun, Haoyan Huo +5
Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one majo…
NLS: an accurate and yet easy-to-interpret regression method
Victor Coscrato, Marco Henrique de Almeida Inácio, Tiago Botari +1
An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. How…
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
Tiago Botari, Rafael Izbicki, Andre C. P. L. F. de Carvalho
As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One impor…