João M. Pereira

Orcid: 0000-0003-3773-7623

Affiliations:

Instituto de Matemática Pura e Aplicada (IMPA), Rio de Janeiro, RJ, Brazil
University of Texas at Austin, Oden Institute for Computational Sciences, Austin, TX, USA (former)
Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA (former)
Princeton University, The Program in Applied and Computational Mathematics, NJ, USA (former, PhD 2019)

According to our database¹, João M. Pereira authored at least 19 papers between 2017 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

Subspace power method for symmetric tensor decomposition.

[BibT_eX]

[DOI]

Joe Kileel

João M. Pereira

Numer. Algorithms, June, 2026

Multi-context principal component analysis.

[BibT_eX]

[DOI]

CoRR, January, 2026

2025

Multi-subspace power method for decomposing all tensors.

[BibT_eX]

[DOI]

CoRR, October, 2025

Neuro-Spectral Architectures for Causal Physics-Informed Networks.

[BibT_eX]

[DOI]

Christian Júnior de Oliveira

Vitor Balestro

Lucas dos Santos Fernandez

CoRR, September, 2025

Neural Conjugate Flows: A Physics-Informed Architecture with Flow Structure.

[BibT_eX]

[DOI]

Arthur Bizzi

Lucas Nissenbaum

João M. Pereira

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

A Search-Free \({O(1/{k}^{3/2})}\) Homotopy Inexact Proximal-Newton Extragradient Algorithm for Monotone Variational Inequalities.

[BibT_eX]

[DOI]

Maicon Marques Alves

João M. Pereira

Benar Fux Svaiter

SIAM J. Optim., 2024

Neural Conjugate Flows: Physics-informed architectures with flow structure.

[BibT_eX]

[DOI]

Arthur Bizzi

Lucas Nissenbaum

João M. Pereira

CoRR, 2024

2022

Identifying Latent Stochastic Differential Equations.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2022

Tensor Moments of Gaussian Mixture Models: Theory and Applications.

[BibT_eX]

[DOI]

João M. Pereira

Joe Kileel

Tamara G. Kolda

CoRR, 2022

Modeling extremes with d-max-decreasing neural networks.

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2022

2021

Deep Extreme Value Copulas for Estimation and Sampling.

[BibT_eX]

[DOI]

CoRR, 2021

Landscape analysis of an improved power method for tensor decomposition.

[BibT_eX]

[DOI]

Joe Kileel

Timo Klock

João M. Pereira

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning latent stochastic differential equations with variational auto-encoders.

[BibT_eX]

[DOI]

CoRR, 2020

Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Learning Partial Differential Equations From Data Using Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Multireference Alignment Is Easier With an Aperiodic Translation Distribution.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2019

Subspace power method for symmetric tensor decomposition and generalized PCA.

[BibT_eX]

[DOI]

Joe Kileel

João M. Pereira

CoRR, 2019

2018

Estimation in the Group Action Channel.

[BibT_eX]

[DOI]

Emmanuel Abbe

João M. Pereira

Amit Singer

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

2017

Sample complexity of the boolean multireference alignment problem.

[BibT_eX]

[DOI]

Emmanuel Abbe

João M. Pereira

Amit Singer

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

João M. Pereira

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