Search Results for author: Pedro M. Q. Aguiar

Found 6 papers, 5 papers with code

COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization

1 code implementation7 Sep 2021 Manuel Madeira, Renato Negrinho, João Xavier, Pedro M. Q. Aguiar

First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning.

Stochastic Optimization

Sparse Continuous Distributions and Fenchel-Young Losses

1 code implementation4 Aug 2021 André F. T. Martins, Marcos Treviso, António Farinhas, Pedro M. Q. Aguiar, Mário A. T. Figueiredo, Mathieu Blondel, Vlad Niculae

In contrast, for finite domains, recent work on sparse alternatives to softmax (e. g., sparsemax, $\alpha$-entmax, and fusedmax), has led to distributions with varying support.

Audio Classification Question Answering +1

Sparse and Continuous Attention Mechanisms

2 code implementations NeurIPS 2020 André F. T. Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro M. Q. Aguiar, Mário A. T. Figueiredo

Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e. g., Gaussian, Dirichlet, Poisson, and categorical distributions via the softmax transformation).

Machine Translation Question Answering +4

Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization

1 code implementation27 Dec 2019 Lourenço V. Pato, Renato Negrinho, Pedro M. Q. Aguiar

In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections.

object-detection Object Detection

Distributed Optimization With Local Domains: Applications in MPC and Network Flows

1 code implementation8 May 2013 João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel

Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions.

Optimization and Control Information Theory Information Theory

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