586 research outputs found
Deep Learning For Smile Recognition
Inspired by recent successes of deep learning in computer vision, we propose
a novel application of deep convolutional neural networks to facial expression
recognition, in particular smile recognition. A smile recognition test accuracy
of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action
(DISFA) database, significantly outperforming existing approaches based on
hand-crafted features with accuracies ranging from 65.55% to 79.67%. The
novelty of this approach includes a comprehensive model selection of the
architecture parameters, allowing to find an appropriate architecture for each
expression such as smile. This is feasible because all experiments were run on
a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations
on a CPU.Comment: Proceedings of the 12th Conference on Uncertainty Modelling in
Knowledge Engineering and Decision Making (FLINS 2016
A Simple and Correct Even-Odd Algorithm for the Point-in-Polygon Problem for Complex Polygons
Determining if a point is in a polygon or not is used by a lot of
applications in computer graphics, computer games and geoinformatics.
Implementing this check is error-prone since there are many special cases to be
considered. This holds true in particular for complex polygons whose edges
intersect each other creating holes. In this paper we present a simple even-odd
algorithm to solve this problem for complex polygons in linear time and prove
its correctness for all possible points and polygons. We furthermore provide
examples and implementation notes for this algorithm.Comment: Proceedings of the 12th International Joint Conference on Computer
Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP
2017), Volume 1: GRAP
Impact of Biases in Big Data
The underlying paradigm of big data-driven machine learning reflects the
desire of deriving better conclusions from simply analyzing more data, without
the necessity of looking at theory and models. Is having simply more data
always helpful? In 1936, The Literary Digest collected 2.3M filled in
questionnaires to predict the outcome of that year's US presidential election.
The outcome of this big data prediction proved to be entirely wrong, whereas
George Gallup only needed 3K handpicked people to make an accurate prediction.
Generally, biases occur in machine learning whenever the distributions of
training set and test set are different. In this work, we provide a review of
different sorts of biases in (big) data sets in machine learning. We provide
definitions and discussions of the most commonly appearing biases in machine
learning: class imbalance and covariate shift. We also show how these biases
can be quantified and corrected. This work is an introductory text for both
researchers and practitioners to become more aware of this topic and thus to
derive more reliable models for their learning problems
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
In machine learning, a bias occurs whenever training sets are not
representative for the test data, which results in unreliable models. The most
common biases in data are arguably class imbalance and covariate shift. In this
work, we aim to shed light on this topic in order to increase the overall
attention to this issue in the field of machine learning. We propose a scalable
novel framework for reducing multiple biases in high-dimensional data sets in
order to train more reliable predictors. We apply our methodology to the
detection of irregular power usage from real, noisy industrial data. In
emerging markets, irregular power usage, and electricity theft in particular,
may range up to 40% of the total electricity distributed. Biased data sets are
of particular issue in this domain. We show that reducing these biases
increases the accuracy of the trained predictors. Our models have the potential
to generate significant economic value in a real world application, as they are
being deployed in a commercial software for the detection of irregular power
usage
Robust and Risk-Sensitive Markov Decision Processes with Applications to Dynamic Optimal Reinsurance
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