103,773 research outputs found
Attend and Diagnose: Clinical Time Series Analysis using Attention Models
With widespread adoption of electronic health records, there is an increased
emphasis for predictive models that can effectively deal with clinical
time-series data. Powered by Recurrent Neural Network (RNN) architectures with
Long Short-Term Memory (LSTM) units, deep neural networks have achieved
state-of-the-art results in several clinical prediction tasks. Despite the
success of RNNs, its sequential nature prohibits parallelized computing, thus
making it inefficient particularly when processing long sequences. Recently,
architectures which are based solely on attention mechanisms have shown
remarkable success in transduction tasks in NLP, while being computationally
superior. In this paper, for the first time, we utilize attention models for
clinical time-series modeling, thereby dispensing recurrence entirely. We
develop the \textit{SAnD} (Simply Attend and Diagnose) architecture, which
employs a masked, self-attention mechanism, and uses positional encoding and
dense interpolation strategies for incorporating temporal order. Furthermore,
we develop a multi-task variant of \textit{SAnD} to jointly infer models with
multiple diagnosis tasks. Using the recent MIMIC-III benchmark datasets, we
demonstrate that the proposed approach achieves state-of-the-art performance in
all tasks, outperforming LSTM models and classical baselines with
hand-engineered features.Comment: AAAI 201
Two Social Worlds: Social Correlates and Stability of Adolescent Status Groups
Examined adolescents\u27 peer group status in high school using self-report, peer nominations, and archival data collected during 2 consecutive school yrs. 408 students participated in the 1st yr, and 404 students participated in the 2nd yr. 60% of the 2nd yr Ss had also participated in the 1st yr. Higher status students (popular and controversial) had more close friends, engaged more frequently in peer activities, and self-disclosed more than lower status students (rejected and neglected). They were also more involved in extracurricular school activities and received more social honors from their schoolmates. Although the higher status students were more alike than different, controversial adolescents did report more self-disclosure and dating behavior than popular students. Lower status students were also highly similar, although rejected students reported lower grades
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
Correlation, hierarchies, and networks in financial markets
We discuss some methods to quantitatively investigate the properties of
correlation matrices. Correlation matrices play an important role in portfolio
optimization and in several other quantitative descriptions of asset price
dynamics in financial markets. Specifically, we discuss how to define and
obtain hierarchical trees, correlation based trees and networks from a
correlation matrix. The hierarchical clustering and other procedures performed
on the correlation matrix to detect statistically reliable aspects of the
correlation matrix are seen as filtering procedures of the correlation matrix.
We also discuss a method to associate a hierarchically nested factor model to a
hierarchical tree obtained from a correlation matrix. The information retained
in filtering procedures and its stability with respect to statistical
fluctuations is quantified by using the Kullback-Leibler distance.Comment: 37 pages, 9 figures, 3 table
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