3 research outputs found
Recurrent Neural Networks for Time Series Forecasting
Time series forecasting is difficult. It is difficult even for recurrent
neural networks with their inherent ability to learn sequentiality. This
article presents a recurrent neural network based time series forecasting
framework covering feature engineering, feature importances, point and interval
predictions, and forecast evaluation. The description of the method is followed
by an empirical study using both LSTM and GRU networks
Understanding Memory Modules on Learning Simple Algorithms
Recent work has shown that memory modules are crucial for the generalization
ability of neural networks on learning simple algorithms. However, we still
have little understanding of the working mechanism of memory modules. To
alleviate this problem, we apply a two-step analysis pipeline consisting of
first inferring hypothesis about what strategy the model has learned according
to visualization and then verify it by a novel proposed qualitative analysis
method based on dimension reduction. Using this method, we have analyzed two
popular memory-augmented neural networks, neural Turing machine and
stack-augmented neural network on two simple algorithm tasks including
reversing a random sequence and evaluation of arithmetic expressions. Results
have shown that on the former task both models can learn to generalize and on
the latter task only the stack-augmented model can do so. We show that
different strategies are learned by the models, in which specific categories of
input are monitored and different policies are made based on that to change the
memory.Comment: Accepted at the XAI Workshop in IJCAI 201
Teaching a Machine to Diagnose a Heart Disease; Beginning from digitizing scanned ECGs to detecting the Brugada Syndrome (BrS)
Medical diagnoses can shape and change the life of a person drastically.
Therefore, it is always best advised to collect as much evidence as possible to
be certain about the diagnosis. Unfortunately, in the case of the Brugada
Syndrome (BrS), a rare and inherited heart disease, only one diagnostic
criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In
the following treatise, we question whether the investigation of ECG strips by
the means of machine learning methods improves the detection of BrS positive
cases and hence, the diagnostic process. We propose a pipeline that reads in
scanned images of ECGs, and transforms the encaptured signals to digital
time-voltage data after several processing steps. Then, we present a long
short-term memory (LSTM) classifier that is built based on the previously
extracted data and that makes the diagnosis. The proposed pipeline
distinguishes between three major types of ECG images and recreates each
recorded lead signal. Features and quality are retained during the digitization
of the data, albeit some encountered issues are not fully removed (Part I).
Nevertheless, the results of the aforesaid program are suitable for further
investigation of the ECG by a computational method such as the proposed
classifier which proves the concept and could be the architectural basis for
future research (Part II). This thesis is divided into two parts as they are
part of the same process but conceptually different. It is hoped that this work
builds a new foundation for computational investigations in the case of the BrS
and its diagnosis