2,358 research outputs found
Autoencoders for strategic decision support
In the majority of executive domains, a notion of normality is involved in
most strategic decisions. However, few data-driven tools that support strategic
decision-making are available. We introduce and extend the use of autoencoders
to provide strategically relevant granular feedback. A first experiment
indicates that experts are inconsistent in their decision making, highlighting
the need for strategic decision support. Furthermore, using two large
industry-provided human resources datasets, the proposed solution is evaluated
in terms of ranking accuracy, synergy with human experts, and dimension-level
feedback. This three-point scheme is validated using (a) synthetic data, (b)
the perspective of data quality, (c) blind expert validation, and (d)
transparent expert evaluation. Our study confirms several principal weaknesses
of human decision-making and stresses the importance of synergy between a model
and humans. Moreover, unsupervised learning and in particular the autoencoder
are shown to be valuable tools for strategic decision-making
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From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis
Image analysis based on machine learning has gained prominence with the advent of deep learning, particularly in medical imaging. To be effective in addressing challenging image analysis tasks, however, conventional deep neural networks require large corpora of annotated training data, which are unfortunately scarce in the medical domain, thus often rendering fully-supervised learning strategies ineffective.This thesis devises for use in a variety of medical image analysis applications a series of novel deep learning methods, ranging from fully-supervised, single-task learning to scarcely-supervised, multi-task learning that makes efficient use of annotated training data. Specifically, its main contributions include (1) fully-supervised, single-task learning for the segmentation of pulmonary lobes from chest CT scans and the analysis of scoliosis from spine X-ray images; (2) supervised, single-task, domain-generalized pulmonary segmentation in chest X-ray images and retinal vasculature segmentation in fundoscopic images; (3) largely-unsupervised, multiple-task learning via deep generative modeling for the joint synthesis and classification of medical image data; and (4) partly-supervised, multiple-task learning for the combined segmentation and classification of chest and spine X-ray images
Learning Off-Road Terrain Traversability with Self-Supervisions Only
Estimating the traversability of terrain should be reliable and accurate in
diverse conditions for autonomous driving in off-road environments. However,
learning-based approaches often yield unreliable results when confronted with
unfamiliar contexts, and it is challenging to obtain manual annotations
frequently for new circumstances. In this paper, we introduce a method for
learning traversability from images that utilizes only self-supervision and no
manual labels, enabling it to easily learn traversability in new circumstances.
To this end, we first generate self-supervised traversability labels from past
driving trajectories by labeling regions traversed by the vehicle as highly
traversable. Using the self-supervised labels, we then train a neural network
that identifies terrains that are safe to traverse from an image using a
one-class classification algorithm. Additionally, we supplement the limitations
of self-supervised labels by incorporating methods of self-supervised learning
of visual representations. To conduct a comprehensive evaluation, we collect
data in a variety of driving environments and perceptual conditions and show
that our method produces reliable estimations in various environments. In
addition, the experimental results validate that our method outperforms other
self-supervised traversability estimation methods and achieves comparable
performances with supervised learning methods trained on manually labeled data.Comment: Accepted to IEEE Robotics and Automation Letters. Our video can be
found at https://bit.ly/3YdKan
Semi-supervised and Active Learning Models for Software Fault Prediction
As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets
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