179 citations · 352 across the 24 of their papers we have counts for
24 papers
Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide
Diedre Carmo, Gustavo Pinheiro, Lívia Rodrigues +3
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis. However, many researchers who are new to the field struggle with…
Visconde: Multi-document QA with GPT-3 and Neural Reranking
Jayr Pereira, Robson Fidalgo, Roberto Lotufo +1
This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents. The system, called Viscond…
NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost
Thales Sales Almeida, Thiago Laitz, João Seródio +3
The widespread availability of search API's (both free and commercial) brings the promise of increased coverage and quality of search results for metasearch engines, while decreasi…
Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge
Diedre Carmo, Leticia Rittner, Roberto Lotufo
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge. This ma…
MonoByte: A Pool of Monolingual Byte-level Language Models
Hugo Abonizio, Leandro Rodrigues de Souza, Roberto Lotufo +1
The zero-shot cross-lingual ability of models pretrained on multilingual and even monolingual corpora has spurred many hypotheses to explain this intriguing empirical result. Howev…
A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022
Vitor Jeronymo, Guilherme Rosa, Surya Kallumadi +2
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022. We rely on a receipt that showed to be effective in previous competitions: we focus…
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models
Mirelle Bueno, Carlos Gemmell, Jeffrey Dalton +2
The ability to extrapolate, i.e., to make predictions on sequences that are longer than those presented as training examples, is a challenging problem for current deep learning mod…
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +4
Recent work has shown that small distilled language models are strong competitors to models that are orders of magnitude larger and slower in a wide range of information retrieval…
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Guilherme Moraes Rosa, Luiz Bonifacio, Vitor Jeronymo +3
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment…
Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents
Ramon Pires, Fábio C. de Souza, Guilherme Rosa +2
A typical information extraction pipeline consists of token- or span-level classification models coupled with a series of pre- and post-processing scripts. In a production pipeline…
On the ability of monolingual models to learn language-agnostic representations
Leandro Rodrigues de Souza, Rodrigo Nogueira, Roberto Lotufo
Pretrained multilingual models have become a de facto default approach for zero-shot cross-lingual transfer. Previous work has shown that these models are able to achieve cross-lin…
mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
Luiz Bonifacio, Vitor Jeronymo, Hugo Queiroz Abonizio +4
The MS MARCO ranking dataset has been widely used for training deep learning models for IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this…
A cost-benefit analysis of cross-lingual transfer methods
Guilherme Moraes Rosa, Luiz Henrique Bonifacio, Leandro Rodrigues de Souza +2
An effective method for cross-lingual transfer is to fine-tune a bilingual or multilingual model on a supervised dataset in one language and evaluating it on another language in a…
Yes, BM25 is a Strong Baseline for Legal Case Retrieval
Guilherme Moraes Rosa, Ruan Chaves Rodrigues, Roberto Lotufo +1
We describe our single submission to task 1 of COLIEE 2021. Our vanilla BM25 got second place, well above the median of submissions. Code is available at https://github.com/neuralm…
Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
Gabriela Surita, Rodrigo Nogueira, Roberto Lotufo
What are the latent questions on some textual data? In this work, we investigate using question generation models for exploring a collection of documents. Our method, dubbed corpus…
Lite Training Strategies for Portuguese-English and English-Portuguese Translation
Alexandre Lopes, Rodrigo Nogueira, Roberto Lotufo +1
Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models. In this work, we investigate the use of…
PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data
Diedre Carmo, Marcos Piau, Israel Campiotti +2
In natural language processing (NLP), there is a need for more resources in Portuguese, since much of the data used in the state-of-the-art research is in other languages. In this…
Electricity Theft Detection with self-attention
Paulo Finardi, Israel Campiotti, Gustavo Plensack +4
In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumpti…
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
Diedre Carmo, Bruna Silva, Clarissa Yasuda +2
Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segment…
Portuguese Named Entity Recognition using BERT-CRF
Fábio Souza, Rodrigo Nogueira, Roberto Lotufo
Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of a trained model to downstream natural language proce…
Extended 2D Consensus Hippocampus Segmentation
Diedre Carmo, Bruna Silva, Clarissa Yasuda +2
Hippocampus segmentation plays a key role in diagnosing various brain disorders such as Alzheimer's disease, epilepsy, multiple sclerosis, cancer, depression and others. Nowadays,…
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Oeslle Lucena, Roberto Souza, Leticia Rittner +2
Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to…
Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping
Oeslle Lucena, Roberto Souza, Letícia Rittner +2
The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-s…
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
Rodrigo Frassetto Nogueira, Roberto de Alencar Lotufo, Rubens Campos Machado
With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate t…