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Found 14 papers, 0 papers with code
Mitigating Bias in Facial Analysis Systems by Incorporating Label Diversity
Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits.
Computing with Subjectivity Lexicons
In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese.
Modeling Pharmacological Effects with Multi-Relation Unsupervised Graph Embedding
Once representations for drugs and diseases are obtained we learn the likelihood of new links (that is, new indications) between drugs and diseases.
Explainable Deep CNNs for MRI-Based Diagnosis of Alzheimer's Disease
Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI).
Assessing the Reliability of Visual Explanations of Deep Models with Adversarial Perturbations
Thus, in this work we propose an objective measure to evaluate the reliability of explanations of deep models.
Automatic Tag Recommendation for Painting Artworks Using Diachronic Descriptions
To validate our method we build a model based on a weakly-supervised neural network for over $5{,}300$ paintings with hand-labeled descriptions made by experts for the paintings of the Brazilian painter Candido Portinari.
Learning Transferable Features for Speech Emotion Recognition
We use transferable features to enable model adaptation from multiple source domains, given the sparseness of speech emotion data and the fact that target domains are short of labeled data.
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation
Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups.
UaiNets: From Unsupervised to Active Deep Anomaly Detection
This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection.
Automated Essay Scoring in the Presence of Biased Ratings
We present features to quantify rater bias based on their comments, and we found that rater bias plays an important role in automated essay scoring.
Deep Active Learning for Anomaly Detection
Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically.
Fast Node Embeddings: Learning Ego-Centric Representations
Representation learning is one of the foundations of Deep Learning and allowed important improvements on several Machine Learning tasks, such as Neural Machine Translation, Question Answering and Speech Recognition.
A Multi-aspect Analysis of Automatic Essay Scoring for Brazilian Portuguese
The AES system proposed employs several features already employed by AES systems for English language.
