no code implementations • 27 Jan 2023 • Megan Leszczynski, Ravi Ganti, Shu Zhang, Krisztian Balog, Filip Radlinski, Fernando Pereira, Arun Tejasvi Chaganty
Conversational recommendation systems (CRSs) enable users to use natural language feedback to control their recommendations, overcoming many of the challenges of traditional recommendation systems.
no code implementations • 4 Aug 2022 • André F. R. Guarda, Nuno M. M. Rodrigues, Manuel Ruivo, Luís Coelho, Abdelrahman Seleem, Fernando Pereira
This document describes a deep learning-based point cloud geometry codec and a deep learning-based point cloud joint geometry and colour codec, submitted to the Call for Proposals on JPEG Pleno Point Cloud Coding issued in January 2022.
1 code implementation • 5 Aug 2021 • Alireza Javaheri, Catarina Brites, Fernando Pereira, João Ascenso
Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images.
Ranked #1 on Point Cloud Quality Assessment on M-PCCD
1 code implementation • CVPR 2021 • Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia, Ali Etemad
We validate the performance of our proposed architecture in the context of two multi-perspective visual recognition tasks namely lip reading and face recognition.
no code implementations • 10 Jan 2021 • Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia
A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests.
1 code implementation • NeurIPS 2020 • Haitian Sun, Andrew O. Arnold, Tania Bedrax-Weiss, Fernando Pereira, William W. Cohen
We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs.
no code implementations • 11 May 2019 • Alireza Sepas-Moghaddam, Ali Etemad, Fernando Pereira, Paulo Lobato Correia
In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks.
no code implementations • 3 Jan 2019 • Alireza Sepas-Moghaddam, Fernando Pereira, Paulo Lobato Correia
In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment.
no code implementations • WS 2018 • Jason Baldridge, Tania Bedrax-Weiss, Daphne Luong, Srini Narayanan, Bo Pang, Fernando Pereira, Radu Soricut, Michael Tseng, Yuan Zhang
no code implementations • 25 May 2018 • Alireza Sepas-Moghaddam, Mohammad A. Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira
This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.
no code implementations • 5 Nov 2015 • Ciprian Chelba, Fernando Pereira
In experiments on the one billion words language modeling benchmark, we are able to slightly improve on our previous results which use a different loss function, and employ leave-one-out training on a subset of the main training set.
no code implementations • 16 Jan 2014 • João V. Graça, Kuzman Ganchev, Luisa Coheur, Fernando Pereira, Ben Taskar
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.
no code implementations • NeurIPS 2009 • Kuzman Ganchev, Ben Taskar, Fernando Pereira, João Gama
We apply this new method to learn ﬁrst-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and signiﬁcantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.
no code implementations • NeurIPS 2009 • Samy Bengio, Fernando Pereira, Yoram Singer, Dennis Strelow
Bag-of-words document representations are often used in text, image and video processing.
no code implementations • NeurIPS 2008 • Koby Crammer, Mark Dredze, Fernando Pereira
Confidence-weighted (CW) learning , an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.
no code implementations • NeurIPS 2007 • John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain.
no code implementations • 1 Jun 2007 • John Blitzer, Mark Dredze, Fernando Pereira
Automatic sentiment classification has been extensively studied and applied in recent years.