A Survey on Deep Learning Methods for Robot Vision

--Abstract
Deep learning has allowed a paradigm shift in pattern recognition, from usinghand-crafted features together with statistical classifiers to usinggeneral-purpose learning procedures for learning data-driven representations,features, and classifiers together. The application of this new paradigm hasbeen particularly successful in computer vision, in which the development ofdeep learning methods for vision applications has become a hot research topic.Given that deep learning has already attracted the attention of the robotvision community, the main purpose of this survey is to address the use of deeplearning in robot vision. To achieve this, a comprehensive overview of deeplearning and its usage in computer vision is given, that includes a descriptionof the most frequently used neural models and their main application areas.Then, the standard methodology and tools used for designing deep-learning basedvision systems are presented. Afterwards, a review of the principal work usingdeep learning in robot vision is presented, as well as current and futuretrends related to the use of deep learning in robotics. This survey is intendedto be a guide for the developers of robot vision systems.

2018-03-28T21:37:14Z

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Link : http://arxiv.org/abs/1803.10862v1

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

--Abstract
Deep learning is a branch of artificial intelligence where networks of simpleinterconnected units are used to extract patterns from data in order to solvecomplex problems. Deep learning algorithms have shown groundbreakingperformance in a variety of sophisticated tasks, especially those related toimages. They have often matched or exceeded human performance. Since themedical field of radiology mostly relies on extracting useful information fromimages, it is a very natural application area for deep learning, and researchin this area has rapidly grown in recent years. In this article, we review theclinical reality of radiology and discuss the opportunities for application ofdeep learning algorithms. We also introduce basic concepts of deep learningincluding convolutional neural networks. Then, we present a survey of theresearch in deep learning applied to radiology. We organize the studies by thetypes of specific tasks that they attempt to solve and review the broad rangeof utilized deep learning algorithms. Finally, we briefly discuss opportunitiesand challenges for incorporating deep learning in the radiology practice of thefuture.

2018-02-10T04:00:55Z

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Link : http://arxiv.org/abs/1802.08717v1

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This device removes blood clots without surgery.
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NEW YOUTUBE VIDEO: This pedals-less trike lets people with movement disabilities walk.

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#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018
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#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018
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Advanced Analytics with Spark — S. Ryza и др. (en) 2017
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Advanced Analytics with Spark — S. Ryza и др. (en) 2017
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Allocated time to media per person

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Source:Nielsen
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#google_assistant
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https://www.analyticsindiamag.com/iit-hyd-btech-ai-admission-jee-advanced/
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#مقاله
Cross-entropy loss can be equated to the Jensen-Shannon distance metric, and it was shown in early 2017 by Arjovsky et al. that this metric would fail in some cases and not point in the right direction in other cases. This group showed that the Wasserstein distance metric (also known as the earth mover or EM distance) worked and worked better in many more cases.

https://arxiv.org/abs/1701.07875

#GAN @WGAN
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#datascience #machinelearning #learning #AI #beginner
Coursera MOOC for "Neural Networks for Machine Learning" by Geoffrey Hinton (Known as GodFather of AI) was prepared in 2012. But the lectures are still a good introduction to many of the basic ideas and are available at
https://www.cs.toronto.edu/~hinton/coursera_lectures.html

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#datascience #machinelearning #R #learning #beginner
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Generalization and Expressivity for Deep Nets

--Abstract
Along with the rapid development of deep learning in practice, thetheoretical explanations for its success become urgent. Generalization andexpressivity are two widely used measurements to quantify theoretical behaviorsof deep learning. The expressivity focuses on finding functions expressible bydeep nets but cannot be approximated by shallow nets with the similar number ofneurons. It usually implies the large capacity. The generalization aims atderiving fast learning rate for deep nets. It usually requires small capacityto reduce the variance. Different from previous studies on deep learning,pursuing either expressivity or generalization, we take both factors intoaccount to explore the theoretical advantages of deep nets. For this purpose,we construct a deep net with two hidden layers possessing excellentexpressivity in terms of localized and sparse approximation. Then, utilizingthe well known covering number to measure the capacity, we find that deep netspossess excellent expressive power (measured by localized and sparseapproximation) without enlarging the capacity of shallow nets. As aconsequence, we derive near optimal learning rates for implementing empiricalrisk minimization (ERM) on the constructed deep nets. These resultstheoretically exhibit the advantage of deep nets from learning theoryviewpoints.

2018-03-10T07:41:25Z

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Link : http://arxiv.org/abs/1803.03772v2

Ophthalmic Diagnosis and Deep Learning -- A Survey

--Abstract
This survey paper presents a detailed overview of the applications for deeplearning in ophthalmic diagnosis using retinal imaging techniques. The need ofautomated computer-aided deep learning models for medical diagnosis isdiscussed. Then a detailed review of the available retinal image datasets isprovided. Applications of deep learning for segmentation of optic disk, bloodvessels and retinal layer as well as detection of red lesions arereviewed.Recent deep learning models for classification of retinal diseaseincluding age-related macular degeneration, glaucoma, diabetic macular edemaand diabetic retinopathy are also reported.

2018-12-09T05:57:17Z

@machinelearning_tuts
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Link : http://arxiv.org/abs/1812.07101v1

Deep Embedding Kernel

--Abstract
In this paper, we propose a novel supervised learning method that is calledDeep Embedding Kernel (DEK). DEK combines the advantages of deep learning andkernel methods in a unified framework. More specifically, DEK is a learnablekernel represented by a newly designed deep architecture. Compared withpre-defined kernels, this kernel can be explicitly trained to map data to anoptimized high-level feature space where data may have favorable featurestoward the application. Compared with typical deep learning using SoftMax orlogistic regression as the top layer, DEK is expected to be more generalizableto new data. Experimental results show that DEK has superior performance thantypical machine learning methods in identity detection, classification,regression, dimension reduction, and transfer learning.

2018-04-16T17:25:24Z

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Link : http://arxiv.org/abs/1804.05806v1

Split learning for health: Distributed deep learning without sharing raw patient data

--Abstract
Can health entities collaboratively train deep learning models withoutsharing sensitive raw data? This paper proposes several configurations of adistributed deep learning method called SplitNN to facilitate suchcollaborations. SplitNN does not share raw data or model details withcollaborating institutions. The proposed configurations of splitNN cater topractical settings of i) entities holding different modalities of patient data,ii) centralized and local health entities collaborating on multiple tasks andiii) learning without sharing labels. We compare performance and resourceefficiency trade-offs of splitNN and other distributed deep learning methodslike federated learning, large batch synchronous stochastic gradient descentand show highly encouraging results for splitNN.

2018-12-03T05:43:20Z

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Link : http://arxiv.org/abs/1812.00564v1