497 research outputs found
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Geometric Supervision and Deep Structured Models for Image Segmentation
The task of semantic segmentation aims at understanding an image at a pixel level. Due to its applicability in many areas, such as autonomous vehicles, robotics and medical surgery assistance, semantic segmentation has become an essential task in image analysis. During the last few years a lot of progress have been made for image segmentation algorithms, mainly due to the introduction of deep learning methods, in particular the use of Convolutional Neural Networks (CNNs). CNNs are powerful for modeling complex connections between input and output data but have two drawbacks when it comes to semantic segmentation. Firstly, CNNs lack the ability to directly model dependent output structures, for instance, explicitly enforcing properties such as label smoothness and coherence. This drawback motivates the use of Conditional Random Fields (CRFs), applied as a post-processing step in semantic segmentation. Secondly, training CNNs requires large amounts of annotated data. For segmentation this amounts to dense, pixel-level, annotations that are very time-consuming to acquire.This thesis summarizes the content of five papers addressing the two aforementioned drawbacks of CNNs. The first two papers present methods on how geometric 3D models can be used to improve segmentation models. The 3D models can be created with little human labour and can be used as a supervisory signal to improve the robustness of semantic segmentation and long-term visual localization methods. The last three papers focuses on models combining CNNs and CRFs for semantic segmentation. The models consist of a CNN capable of learning complex image features coupled with a CRF capable of learning dependencies between output variables. Emphasis has been on creating models that are possible to train end-to-end, giving the CNN and the CRF a chance to learn how to interact and exploit complementary information to achieve better performance
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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