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Found 7 papers, 0 papers with code
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).
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.
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.
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.
