Search Results for author:
Found 11 papers, 2 papers with code
Functional Autoencoder for Smoothing and Representation Learning
A common pipeline in functional data analysis is to first convert the discretely observed data to smooth functions, and then represent the functions by a finite-dimensional vector of coefficients summarizing the information.
Neural Networks for Scalar Input and Functional Output
In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs.
Neuroimaging Feature Extraction using a Neural Network Classifier for Imaging Genetics
A major issue in the association of genes to neuroimaging phenotypes is the high dimension of both genetic data and neuroimaging data.
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis
Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features.
A moment-matching metric for latent variable generative models
The solution we propose is a new metric for model comparison or regularization that relies on moments.
Introducing a new high-resolution handwritten digits data set with writer characteristics
It contains high-resolution images of handwritten digits together with various writer characteristics which are not available in the well-known MNIST database.
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group
We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues.
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis
In the following short article we adapt a new and popular machine learning model for inference on medical data sets.
BEST : A decision tree algorithm that handles missing values
The main contribution of this paper is the development of a new decision tree algorithm.
Predicting University Students' Academic Success and Major using Random Forests
In this article, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analysed.
Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agents Location Using Hidden Markov Models
We propose to use a supervised machine learning technique to track the location of a mobile agent in real time.
