Diego Parente Paiva Mesquita

According to our database, Diego Parente Paiva Mesquita authored at least 37 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

In-n-Out: Calibrating Graph Neural Networks for Link Prediction.

Erik Nascimento, Diego Mesquita, Samuel Kaski, Amauri H. Souza

CoRR, 2024

2023

Thin and Deep Gaussian Processes.

Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João P. P. Gomes, Diego Mesquita, César Lincoln C. Mattos

CoRR, 2023

Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets.

Tiago da Silva, Eliezer Silva, Adèle H. Ribeiro, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita

CoRR, 2023

Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition.

Yuling Yao, Luiz Max Carvalho, Diego Mesquita, Yann McLatchie

CoRR, 2023

Thin and deep Gaussian processes.

Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João Paulo Pordeus Gomes, Diego Mesquita, César C. Lincoln Mattos

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Distill n' Explain: explaining graph neural networks using simple surrogates.

Tamara A. Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri H. Souza

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Bayesian Multilateration.

Alisson S. C. Alencar, César L. C. Mattos, João P. P. Gomes, Diego Mesquita

IEEE Signal Process. Lett., 2022

Provably expressive temporal graph networks.

Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas K. Garg

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Analysis of Bug-Fixing Time using Report Data.

Renan Gomes Vieira, Diego Mesquita, César Lincoln C. Mattos, Ricardo Britto, Lincoln S. Rocha, João Gomes

Proceedings of the ESEM '22: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement, Helsinki, Finland, September 19, 2022

ConveXplainer for Graph Neural Networks.

Tamara A. Pereira, Erik Jhones F. do Nascimento, Diego Mesquita, Amauri H. Souza

Proceedings of the Intelligent Systems - 11th Brazilian Conference, 2022

Parallel MCMC Without Embarrassing Failures.

Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Advances in distributed Bayesian inference and graph neural networks.

Diego Mesquita

PhD thesis, 2021

Federated stochastic gradient Langevin dynamics.

Khaoula el Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions.

Erik Jhones F. do Nascimento, Amauri H. Souza, Diego Mesquita

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Learning GPLVM with arbitrary kernels using the unscented transformation.

Daniel Augusto de Souza, Diego P. P. Mesquita, João Paulo Pordeus Gomes, César Lincoln C. Mattos

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

LS-SVR as a Bayesian RBF Network.

Diego P. P. Mesquita, Luis A. Freitas, João P. P. Gomes, César L. C. Mattos

IEEE Trans. Neural Networks Learn. Syst., 2020

A sparse linear regression model for incomplete datasets.

Marcelo B. A. Veras, Diego P. P. Mesquita, César Lincoln C. Mattos, João P. P. Gomes

Pattern Anal. Appl., 2020

Variance reduction for distributed stochastic gradient MCMC.

Khaoula el Mekkaoui, Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski

CoRR, 2020

Rethinking pooling in graph neural networks.

Diego P. P. Mesquita, Amauri H. Souza Jr., Samuel Kaski

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Artificial Neural Networks with Random Weights for Incomplete Datasets.

Diego P. P. Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues

Neural Process. Lett., 2019

Gaussian kernels for incomplete data.

Diego P. P. Mesquita, João P. P. Gomes, Francesco Corona, Amauri Holanda de Souza Júnior, Juvêncio S. Nobre

Appl. Soft Comput., 2019

Embarrassingly Parallel MCMC using Deep Invertible Transformations.

Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

2018

Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine.

Weslley L. Caldas, João P. P. Gomes, Diego P. P. Mesquita

New Gener. Comput., 2018

Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm.

Diego P. P. Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues, Saulo A. F. Oliveira, Roberto Kawakami Harrop Galvão

Appl. Soft Comput., 2018

2017

Ensemble of Efficient Minimal Learning Machines for Classification and Regression.

Diego P. P. Mesquita, João P. P. Gomes, Amauri Holanda de Souza Júnior

Neural Process. Lett., 2017

Euclidean distance estimation in incomplete datasets.

Diego Parente Paiva Mesquita, João P. P. Gomes, Amauri Holanda Souza Júnior, Juvêncio Santos Nobre

Neurocomputing, 2017

Forward Stagewise Regression on Incomplete Datasets.

Marcelo B. A. Veras, Diego P. P. Mesquita, João P. P. Gomes, Amauri H. Souza Júnior, Guilherme A. Barreto

Proceedings of the Advances in Computational Intelligence, 2017

A Robust Minimal Learning Machine based on the M-Estimator.

João P. P. Gomes, Diego P. P. Mesquita, Ananda Freire, Amauri H. Souza Júnior, Tommi Kärkkäinen

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Classification with reject option for software defect prediction.

Diego Parente Paiva Mesquita, Lincoln S. Rocha, João P. P. Gomes, Ajalmar R. da Rocha Neto

Appl. Soft Comput., 2016

Radial Basis Function Neural Networks for Datasets with Missing Values.

Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes

Proceedings of the Intelligent Systems Design and Applications, 2016

Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness.

Diego P. P. Mesquita, Antônio C. Araújo Neto, Jose Queiroz Neto, João P. P. Gomes, Leonardo Ramos Rodrigues

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values.

Diego P. P. Mesquita, João P. P. Gomes, Leonardo Ramos Rodrigues

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Extreme Learning Machines for Datasets with Missing Values Using the Unscented Transform.

Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator.

Filipe F. R. Damasceno, Marcelo B. A. Veras, Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Carlos Eduardo Fisch de Brito

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

Co-MLM: A SSL Algorithm Based on the Minimal Learning Machine.

Weslley L. Caldas, João Paulo Pordeus Gomes, Michelle G. Cacais, Diego Parente Paiva Mesquita

Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

2015

Ensemble of Minimal Learning Machines for Pattern Classification.

Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Amauri Holanda Souza Júnior

Proceedings of the Advances in Computational Intelligence, 2015

A Minimal Learning Machine for Datasets with Missing Values.

Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Amauri H. Souza Jr.

Proceedings of the Neural Information Processing - 22nd International Conference, 2015