1 code implementation • 20 Oct 2023 • Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil, Seiji Takeda, Hiroshi Kajino, Renato Cerqueira
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
no code implementations • 22 Jun 2023 • Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez, Renato Cerqueira, Dan Sanders, Kristin Schmidt, Dmitry Zubarev
Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.
no code implementations • 9 Mar 2023 • Carlos Raoni Mendes, Emilio Vital Brazil, Vinicius Segura, Renato Cerqueira
Evaluating the potential of a prospective candidate is a common task in multiple decision-making processes in different industries.
no code implementations • 9 Mar 2023 • Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real, Leonardo Azevedo, Vinicius Segura, Luiz Zerkowski, Renato Cerqueira
Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table.
no code implementations • 5 Nov 2022 • Dmitry Zubarev, Carlos Raoni Mendes, Emilio Vital Brazil, Renato Cerqueira, Kristin Schmidt, Vinicius Segura, Juliana Jansen Ferreira, Dan Sanders
There is an increasing need in our society to achieve faster advances in Science to tackle urgent problems, such as climate changes, environmental hazards, sustainable energy systems, pandemics, among others.
no code implementations • 30 Sep 2020 • Renan Souza, Leonardo G. Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
no code implementations • 9 Oct 2019 • Renan Souza, Leonardo Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, Marco A. S. Netto
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
no code implementations • 10 May 2019 • Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil, Bianca Zadrozny
We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.
no code implementations • 26 Mar 2019 • Reinaldo Mozart Silva, Lais Baroni, Rodrigo S. Ferreira, Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil
In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation.