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João M. Pereira

7 accepted papers

2025

FLOWING: Implicit Neural Flows for Structure-Preserving Morphing

Arthur Bizzi, Matias Grynberg Portnoy, Vitor Pereira Matias, Daniel Perazzo, João Paulo Silva do Monte Lima, Luiz Velho +4
NeurIPS 2025poster

Morphing is a long-standing problem in vision and computer graphics, requir- ing a time-dependent warping for feature alignment and a blending for smooth interpolation. Recently, multilayer perceptrons (MLPs) have been explored as implicit neural representations (INRs) for modeling such deformations…

Cited by 0Source ↗code ↗Scholar ↗
2025

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

Arthur Bizzi, Lucas Nissenbaum, João M. Pereira
AAAI 2025technical

We introduce Neural Conjugate Flows (NCF), a class of neural-network architectures equipped with exact flow structure. By leveraging topological conjugation, we prove that these networks are not only naturally isomorphic to a continuous group, but are also universal approximators for flows of ordina…

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2025

Neuro-Spectral Architectures for Causal Physics-Informed Networks

Arthur Bizzi, Leonardo M. Moreira, Márcio Marques, Leonardo Mendonça, Christian Júnior de Oliveira, Vitor Balestro +6
NeurIPS 2025poster

Physics-Informed Neural Networks (PINNs) have emerged as a powerful frame- work for solving partial differential equations (PDEs). However, standard MLP- based PINNs often fail to converge when dealing with complex initial value problems, leading to solutions that violate causality and suffer from a…

Cited by 0Source ↗code ↗Scholar ↗
2022

Modeling extremes with $d$-max-decreasing neural networks

Ali Hasan, Khalil Elkhalil, Yuting Ng, João M. Pereira, Sina Farsiu, Jose Blanchet +1
UAI 2022poster

We propose a neural network architecture that enables non-parametric calibration and generation of multivariate extreme value distributions (MEVs). MEVs arise from Extreme Value Theory (EVT) as the necessary class of models when extrapolating a distributional fit over large spatial and temporal sca…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2021

Landscape analysis of an improved power method for tensor decomposition

Joe Kileel, Timo Klock, João M. Pereira
NeurIPS 2021poster

In this work, we consider the optimization formulation for symmetric tensor decomposition recently introduced in the Subspace Power Method (SPM) of Kileel and Pereira. Unlike popular alternative functionals for tensor decomposition, the SPM objective function has the desirable properties that its m…

Cited by 12Source ↗PDF ↗Scholar ↗
2020

Learning Partial Differential Equations From Data Using Neural Networks

Ali Hasan, João M. Pereira, Robert J. Ravier, Sina Farsiu, Vahid Tarokh
ICASSP 2020accepted

We develop a framework for estimating unknown partial differential equations (PDEs) from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, our method interpolates the samples using a neural network, and extracts the PDE by equating derivatives of the ne…

Cited by 0Source ↗Scholar ↗
2020

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

Yuting Ng, João M. Pereira, Denis Garagic, Vahid Tarokh
ICASSP 2020accepted

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propos…

Cited by 0Source ↗Scholar ↗