Implementing Contextual Neural Networks in Distributed Machine Learning Framework

2018 ◽  
pp. 212-223 ◽  
Author(s):  
Bartosz Jerzy Janusz ◽  
Krzysztof Wołk
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Learning Framework ◽  
Distributed Machine Learning

Object Detection using Feature Mining in a Distributed Machine Learning Framework

2017 ◽  
Author(s):  
Arne Ehlers
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Training Data ◽  
Visual Object ◽  
Ensemble Classifiers ◽  
Adaptive Boosting ◽  
Learning Framework ◽  
Theory Of Evidence ◽  
Feature Mining ◽  
Distributed Machine Learning

This dissertation addresses the problem of visual object detection based on machine-learned classifiers. A distributed machine learning framework is developed to learn detectors for several object classes creating cascaded ensemble classifiers by the Adaptive Boosting algorithm. Methods are proposed that enhance several components of an object detection framework: At first, the thesis deals with augmenting the training data in order to improve the performance of object detectors learned from sparse training sets. Secondly, feature mining strategies are introduced to create feature sets that are customized to the object class to be detected. Furthermore, a novel class of fractal features is proposed that allows to represent a wide variety of shapes. Thirdly, a method is introduced that models and combines internal confidences and uncertainties of the cascaded detector using Dempster’s theory of evidence in order to increase the quality of the post-processing. ...

scholarly journals All-optical machine learning using diffractive deep neural networks

Science ◽  
2018 ◽  
Vol 361 (6406) ◽  
pp. 1004-1008 ◽  
Author(s):  
Xing Lin ◽  
Yair Rivenson ◽  
Nezih T. Yardimci ◽  
Muhammed Veli ◽  
Yi Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Feature Detection ◽  
Learning Framework ◽  
Fashion Products ◽  
Complex Functions ◽  
All Optical ◽  
Computer Based ◽  
Classification Of Images

Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D2NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D2NNs.

scholarly journals DeepEP: a deep learning framework for identifying essential proteins

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Min Zeng ◽  
Min Li ◽  
Fang-Xiang Wu ◽  
Yaohang Li ◽  
Yi Pan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Sampling Method ◽  
Ppi Network ◽  
Essential Proteins ◽  
Learning Framework ◽  
Multi Scale ◽  
Topological Features

Abstract Background Essential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Results We develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins. Conclusion We demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.

scholarly journals Emotional quantification of soundscapes by learning between samples

2020 ◽  
Vol 79 (41-42) ◽  
pp. 30387-30395
Author(s):  
Stavros Ntalampiras
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Emotional Responses ◽  
Machine Learning Techniques ◽  
Specific Domain ◽  
Learning Framework ◽  
Learning Techniques ◽  
Wide Range

Abstract Predicting the emotional responses of humans to soundscapes is a relatively recent field of research coming with a wide range of promising applications. This work presents the design of two convolutional neural networks, namely ArNet and ValNet, each one responsible for quantifying arousal and valence evoked by soundscapes. We build on the knowledge acquired from the application of traditional machine learning techniques on the specific domain, and design a suitable deep learning framework. Moreover, we propose the usage of artificially created mixed soundscapes, the distributions of which are located between the ones of the available samples, a process that increases the variance of the dataset leading to significantly better performance. The reported results outperform the state of the art on a soundscape dataset following Schafer’s standardized categorization considering both sound’s identity and the respective listening context.

MACHINE LEARNING FOR SOFTWARE

2019 ◽  
pp. 251-255
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Soft Computing ◽  
Computing Technology

The study machine learning for software based on Soft Computing technology. It analyzes Soft Computing components. Their use in software, their advantages and challenges are studied. Machine learning and its features are highlighted. The functions and features of neural networks are clarified, and recommendations were given.

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