21 research outputs found
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part detection task helps to regularize the network, directing it to
converge to a good solution. We report competitive and state-of-art results on
several data sets. We also empirically show that the learned neurons in the
middle layer of our network are tuned to localized body parts
Deep Patient Representation of Clinical Notes via Multi-Task Learning for Mortality Prediction.
We propose a deep learning-based multi-task learning (MTL) architecture focusing on patient mortality predictions from clinical notes. The MTL framework enables the model to learn a patient representation that generalizes to a variety of clinical prediction tasks. Moreover, we demonstrate how MTL enables small but consistent gains on a single classification task (e.g., in-hospital mortality prediction) simply by incorporating related tasks (e.g., 30-day and 1-year mortality prediction) into the MTL framework. To accomplish this, we utilize a multi-level Convolutional Neural Network (CNN) associated with a MTL loss component. The model is evaluated with 3, 5, and 20 tasks and is consistently able to produce a higher-performing model than a single-task learning (STL) classifier. We further discuss the effect of the multi-task model on other clinical outcomes of interest, including being able to produce high-quality representations that can be utilized to great effect by simpler models. Overall, this study demonstrates the efficiency and generalizability of MTL across tasks that STL fails to leverage
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
Sparse multitask regression for identifying common mechanism of response to therapeutic targets
Motivation: Molecular association of phenotypic responses is an important step in hypothesis generation and for initiating design of new experiments. Current practices for associating gene expression data with multidimensional phenotypic data are typically (i) performed one-to-one, i.e. each gene is examined independently with a phenotypic index and (ii) tested with one stress condition at a time, i.e. different perturbations are analyzed separately. As a result, the complex coordination among the genes responsible for a phenotypic profile is potentially lost. More importantly, univariate analysis can potentially hide new insights into common mechanism of response
Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning
Motivation: Recent advances in brain imaging and high-throughput genotyping techniques enable new approaches to study the influence of genetic and anatomical variations on brain functions and disorders. Traditional association studies typically perform independent and pairwise analysis among neuroimaging measures, cognitive scores and disease status, and ignore the important underlying interacting relationships between these units