7,453 research outputs found
Feedback-prop: Convolutional Neural Network Inference under Partial Evidence
We propose an inference procedure for deep convolutional neural networks
(CNNs) when partial evidence is available. Our method consists of a general
feedback-based propagation approach (feedback-prop) that boosts the prediction
accuracy for an arbitrary set of unknown target labels when the values for a
non-overlapping arbitrary set of target labels are known. We show that existing
models trained in a multi-label or multi-task setting can readily take
advantage of feedback-prop without any retraining or fine-tuning. Our
feedback-prop inference procedure is general, simple, reliable, and works on
different challenging visual recognition tasks. We present two variants of
feedback-prop based on layer-wise and residual iterative updates. We experiment
using several multi-task models and show that feedback-prop is effective in all
of them. Our results unveil a previously unreported but interesting dynamic
property of deep CNNs. We also present an associated technical approach that
takes advantage of this property for inference under partial evidence in
general visual recognition tasks.Comment: Accepted to CVPR 201
An ontology enhanced parallel SVM for scalable spam filter training
This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart
Asynchronous Multi-Context Systems
In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 31-37, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-15056
AOSD Ontology 1.0 - Public Ontology of Aspect-Orientation
This report presents a Common Foundation for Aspect-Oriented Software Development. A Common Foundation is required to enable effective communication and to enable integration of activities within the Network of Excellence. This Common Foundation is realized by developing an ontology, i.e. the shared meaning of terms and concepts in the domain of AOSD. In the first part of this report, we describe the definitions of an initial set of common AOSD terms. There is general agreement on these definitions. In the second part, we describe the Common Foundation task in detail
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