no code implementations • 2 May 2024 • Pedro Mendes, Paolo Romano, David Garlan
In this work, we present a novel technique, named Error-Driven Uncertainty Aware Training (EUAT), which aims to enhance the ability of neural models to estimate their uncertainty correctly, namely to be highly uncertain when they output inaccurate predictions and low uncertain when their output is accurate.
1 code implementation • 18 Apr 2023 • Pedro Mendes
The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case.
1 code implementation • 5 Apr 2023 • Pedro Mendes, Paolo Romano, David Garlan
This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i. e., adversarially trained) models, shedding light on the new challenges and opportunities arising during the HPT process for robust models.
no code implementations • 13 Mar 2022 • Bilal Shaikh, Lucian P. Smith, Dan Vasilescu, Gnaneswara Marupilla, Michael Wilson, Eran Agmon, Henry Agnew, Steven S. Andrews, Azraf Anwar, Moritz E. Beber, Frank T. Bergmann, David Brooks, Lutz Brusch, Laurence Calzone, Kiri Choi, Joshua Cooper, John Detloff, Brian Drawert, Michel Dumontier, G. Bard Ermentrout, James R. Faeder, Andrew P. Freiburger, Fabian Fröhlich, Akira Funahashi, Alan Garny, John H. Gennari, Padraig Gleeson, Anne Goelzer, Zachary Haiman, Joseph L. Hellerstein, Stefan Hoops, Jon C. Ison, Diego Jahn, Henry V. Jakubowski, Ryann Jordan, Matúš Kalaš, Matthias König, Wolfram Liebermeister, Synchon Mandal, Robert McDougal, J. Kyle Medley, Pedro Mendes, Robert Müller, Chris J. Myers, Aurelien Naldi, Tung V. N. Nguyen, David P. Nickerson, Brett G. Olivier, Drashti Patoliya, Loïc Paulevé, Linda R. Petzold, Ankita Priya, Anand K. Rampadarath, Johann M. Rohwer, Ali S. Saglam, Dilawar Singh, Ankur Sinha, Jacky Snoep, Hugh Sorby, Ryan Spangler, Jörn Starruß, Payton J. Thomas, David van Niekerk, Daniel Weindl, Fengkai Zhang, Anna Zhukova, Arthur P. Goldberg, Michael L. Blinov, Herbert M. Sauro, Ion I. Moraru, Jonathan R. Karr
To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.
no code implementations • 24 Feb 2022 • Adelino Leite-Moreira, Afonso Mendes, Afonso Pedrosa, Amândio Rocha-Sousa, Ana Azevedo, André Amaral-Gomes, Cláudia Pinto, Helena Figueira, Nuno Rocha Pereira, Pedro Mendes, Tiago Pimenta
The project aimed to define the rules and develop a technological solution to automatically identify a set of attributes within free-text clinical records written in Portuguese.
1 code implementation • 7 Oct 2021 • Abhishekh Gupta, Pedro Mendes
COPASI is a popular application for simulation and analysis of biochemical networks and their dynamics.
1 code implementation • 5 Aug 2021 • Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan
In the literature on hyper-parameter tuning, a number of recent solutions rely on low-fidelity observations (e. g., training with sub-sampled datasets) in order to efficiently identify promising configurations to be then tested via high-fidelity observations (e. g., using the full dataset).
no code implementations • 9 Nov 2020 • Pedro Mendes, Maria Casimiro, Paolo Romano, David Garlan
This work introduces TrimTuner, the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques to reduce the cost of the optimization process while keeping into account user-specified constraints.
no code implementations • 4 Jun 2018 • Pedro Mendes
Like other types of computational research, modeling and simulation of biological processes (biomodels) is still largely communicated without sufficient detail to allow independent reproduction of results.
no code implementations • 2 Jun 2015 • Catarina Runa Miranda, Pedro Mendes, Pedro Coelho, Xenxo Alvarez, João Freitas, Miguel Sales Dias, Verónica Costa Orvalho
Following this methodology, we also propose two protocols that allow the capturing of facial behaviors under uncontrolled and real-life situations.
no code implementations • LREC 2014 • Miguel B. Almeida, Mariana S. C. Almeida, Andr{\'e} F. T. Martins, Helena Figueira, Pedro Mendes, Cl{\'a}udia Pinto
In this paper, we introduce the Priberam Compressive Summarization Corpus, a new multi-document summarization corpus for European Portuguese.