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ORCIDDiego Parente Paiva Mesquita
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2020 – today
- 2024
[j12]Alan L. S. Matias, João Paulo Pordeus Gomes
, César Lincoln C. Mattos, Ajalmar R. da Rocha Neto, Diego Mesquita:
Bayesian ART for incomplete datasets. Appl. Soft Comput. 163: 111865 (2024)- [j11]Antônio da Silva, Renan Gomes Vieira, Diego P. P. Mesquita, João Paulo Pordeus Gomes, Lincoln S. Rocha:
Towards automatic labeling of exception handling bugs: A case study of 10 years bug-fixing in Apache Hadoop. Empir. Softw. Eng. 29(4): 85 (2024) - [c23]Alan L. S. Matias, César Lincoln C. Mattos, João Paulo Pordeus Gomes, Diego Mesquita:
Amortized Variational Deep Kernel Learning. ICML 2024 - [c22]Tiago da Silva, Luiz Max Carvalho, Amauri H. Souza, Samuel Kaski, Diego Mesquita:
Embarrassingly Parallel GFlowNets. ICML 2024 - [i13]Erik Nascimento, Diego Mesquita, Samuel Kaski, Amauri H. Souza:
In-n-Out: Calibrating Graph Neural Networks for Link Prediction. CoRR abs/2403.04605 (2024) - [i12]Tiago da Silva, Luiz Max Carvalho, Amauri H. Souza, Samuel Kaski, Diego Mesquita:
Embarrassingly Parallel GFlowNets. CoRR abs/2406.03288 (2024) - [i11]Tiago da Silva, Eliezer de Souza da Silva, Diego Mesquita:
On Divergence Measures for Training GFlowNets. CoRR abs/2410.09355 (2024) - 2023
- [c21]Tamara A. Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri H. Souza:
Distill n' Explain: explaining graph neural networks using simple surrogates. AISTATS 2023: 6199-6214 - [c20]Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João Paulo Pordeus Gomes, Diego Mesquita, César Lincoln C. Mattos:
Thin and deep Gaussian processes. NeurIPS 2023 - [i10]Tamara A. Pereira, Erik Nascimento, Lucas E. Resck, Diego Mesquita, Amauri H. Souza:
Distill n' Explain: explaining graph neural networks using simple surrogates. CoRR abs/2303.10139 (2023) - [i9]Yuling Yao, Luiz Max Carvalho, Diego Mesquita, Yann McLatchie:
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition. CoRR abs/2305.07334 (2023) - [i8]Tiago da Silva, Eliezer S. Silva, Adèle H. Ribeiro, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita:
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets. CoRR abs/2309.12032 (2023) - [i7]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:
Thin and Deep Gaussian Processes. CoRR abs/2310.11527 (2023) - 2022
- [j10]Alisson S. C. Alencar, César L. C. Mattos, João P. P. Gomes, Diego Mesquita:
Bayesian Multilateration. IEEE Signal Process. Lett. 29: 962-966 (2022) - [c19]Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi:
Parallel MCMC Without Embarrassing Failures. AISTATS 2022: 1786-1804 - [c18]Tamara A. Pereira, Erik Jhones F. do Nascimento, Diego Mesquita, Amauri H. Souza:
ConveXplainer for Graph Neural Networks. BRACIS (2) 2022: 588-600 - [c17]Renan Gomes Vieira, Diego Mesquita, César Lincoln C. Mattos, Ricardo Britto, Lincoln S. Rocha, João Gomes:
Bayesian Analysis of Bug-Fixing Time using Report Data. ESEM 2022: 57-68 - [c16]Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas Garg:
Provably expressive temporal graph networks. NeurIPS 2022 - [i6]Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi:
Parallel MCMC Without Embarrassing Failures. CoRR abs/2202.11154 (2022) - [i5]Amauri H. Souza, Diego Mesquita, Samuel Kaski, Vikas Garg:
Provably expressive temporal graph networks. CoRR abs/2209.15059 (2022) - 2021
- [b1]Diego Mesquita:
Advances in distributed Bayesian inference and graph neural networks. Aalto University, Espoo, Finland, 2021 - [c15]Daniel Augusto de Souza, Diego P. P. Mesquita, João Paulo Pordeus Gomes, César Lincoln C. Mattos:
Learning GPLVM with arbitrary kernels using the unscented transformation. AISTATS 2021: 451-459 - [c14]Erik Jhones F. do Nascimento, Amauri H. Souza, Diego Mesquita:
Improving Graph Variational Autoencoders with Multi-Hop Simple Convolutions. ESANN 2021 - [c13]Khaoula el Mekkaoui, Diego Mesquita, Paul Blomstedt, Samuel Kaski:
Federated stochastic gradient Langevin dynamics. UAI 2021: 1703-1712 - 2020
- [j9]Marcelo B. A. Veras, Diego P. P. Mesquita, César Lincoln C. Mattos, João P. P. Gomes:
A sparse linear regression model for incomplete datasets. Pattern Anal. Appl. 23(3): 1293-1303 (2020) - [j8]Diego P. P. Mesquita, Luis A. Freitas, João P. P. Gomes, César L. C. Mattos:
LS-SVR as a Bayesian RBF Network. IEEE Trans. Neural Networks Learn. Syst. 31(10): 4389-4393 (2020) - [c12]Diego P. P. Mesquita, Amauri H. Souza Jr., Samuel Kaski:
Rethinking pooling in graph neural networks. NeurIPS 2020 - [i4]Khaoula el Mekkaoui, Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski:
Variance reduction for distributed stochastic gradient MCMC. CoRR abs/2004.11231 (2020) - [i3]Diego P. P. Mesquita, Amauri H. Souza Jr., Samuel Kaski:
Rethinking pooling in graph neural networks. CoRR abs/2010.11418 (2020)
2010 – 2019
- 2019
- [j7]Diego P. P. Mesquita, João P. P. Gomes, Francesco Corona, Amauri Holanda de Souza Júnior, Juvêncio S. Nobre:
Gaussian kernels for incomplete data. Appl. Soft Comput. 77: 356-365 (2019) - [j6]Diego P. P. Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues:
Artificial Neural Networks with Random Weights for Incomplete Datasets. Neural Process. Lett. 50(3): 2345-2372 (2019) - [c11]Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski:
Embarrassingly Parallel MCMC using Deep Invertible Transformations. UAI 2019: 1244-1252 - [i2]Diego P. P. Mesquita, Paul Blomstedt, Samuel Kaski:
Embarrassingly parallel MCMC using deep invertible transformations. CoRR abs/1903.04556 (2019) - [i1]Diego P. P. Mesquita, Luis A. Freitas, João P. P. Gomes, César L. C. Mattos:
LS-SVR as a Bayesian RBF network. CoRR abs/1905.00332 (2019) - 2018
- [j5]Diego P. P. Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues, Saulo A. F. Oliveira, Roberto Kawakami Harrop Galvão:
Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm. Appl. Soft Comput. 70: 1135-1145 (2018) - [j4]Weslley L. Caldas, João P. P. Gomes, Diego P. P. Mesquita:
Fast Co-MLM: An Efficient Semi-supervised Co-training Method Based on the Minimal Learning Machine. New Gener. Comput. 36(1): 41-58 (2018) - 2017
- [j3]Diego Parente Paiva Mesquita, João P. P. Gomes, Amauri Holanda Souza Júnior, Juvêncio Santos Nobre:
Euclidean distance estimation in incomplete datasets. Neurocomputing 248: 11-18 (2017) - [j2]Diego P. P. Mesquita, João P. P. Gomes, Amauri Holanda de Souza Júnior:
Ensemble of Efficient Minimal Learning Machines for Classification and Regression. Neural Process. Lett. 46(3): 751-766 (2017) - [c10]João P. P. Gomes, Diego P. P. Mesquita, Ananda Freire, Amauri H. Souza Júnior, Tommi Kärkkäinen:
A Robust Minimal Learning Machine based on the M-Estimator. ESANN 2017 - [c9]Marcelo B. A. Veras, Diego P. P. Mesquita, João P. P. Gomes, Amauri H. Souza Júnior, Guilherme A. Barreto:
Forward Stagewise Regression on Incomplete Datasets. IWANN (1) 2017: 386-395 - 2016
- [j1]Diego Parente Paiva Mesquita, Lincoln S. Rocha, João P. P. Gomes, Ajalmar R. da Rocha Neto:
Classification with reject option for software defect prediction. Appl. Soft Comput. 49: 1085-1093 (2016) - [c8]Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Leonardo Ramos Rodrigues:
Extreme Learning Machines for Datasets with Missing Values Using the Unscented Transform. BRACIS 2016: 85-90 - [c7]Weslley L. Caldas, João Paulo Pordeus Gomes, Michelle G. Cacais, Diego Parente Paiva Mesquita:
Co-MLM: A SSL Algorithm Based on the Minimal Learning Machine. BRACIS 2016: 97-102 - [c6]Filipe F. R. Damasceno, Marcelo B. A. Veras, Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Carlos Eduardo Fisch de Brito:
Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator. BRACIS 2016: 433-437 - [c5]Diego P. P. Mesquita, João P. P. Gomes, Leonardo Ramos Rodrigues:
K-means for Datasets with Missing Attributes: Building Soft Constraints with Observed and Imputed Values. ESANN 2016 - [c4]Diego P. P. Mesquita, Antônio C. Araújo Neto, Jose Queiroz Neto, João P. P. Gomes, Leonardo Ramos Rodrigues:
Using Robust Extreme Learning Machines to Predict Cotton Yarn Strength and Hairiness. ESANN 2016 - [c3]Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes:
Radial Basis Function Neural Networks for Datasets with Missing Values. ISDA 2016: 108-115 - 2015
- [c2]Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Amauri H. Souza Jr.:
A Minimal Learning Machine for Datasets with Missing Values. ICONIP (1) 2015: 565-572 - [c1]Diego Parente Paiva Mesquita, João Paulo Pordeus Gomes, Amauri Holanda Souza Júnior:
Ensemble of Minimal Learning Machines for Pattern Classification. IWANN (2) 2015: 142-152
Coauthor Index
aka: César L. C. Mattos
aka: Amauri Holanda de Souza Júnior
aka: Amauri Holanda Souza Júnior
aka: Amauri H. Souza Júnior
aka: Amauri H. Souza Jr.
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