Cnn april 8 2020
•Download as PPTX, PDF•
0 likes•92 views
Convolutional neural networks (CNNs) are a type of neural network used in image recognition and processing. CNNs use convolutional layers that apply filters to input volumes to extract features at different spatial locations. Backpropagation is used to train CNNs by propagating errors backwards. CNNs have been successfully applied to tasks like ImageNet classification, object detection, and image captioning. Convolutional layers are the core building blocks of CNNs, applying filters to input volumes to produce activation maps as output.
Report
Share
Report
Share
![Convolutional Neural Network
[1] Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in
Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Exploring the Order of Precedence when Using Contextual Dimensions for Mobile...
This document summarizes a study exploring the order of precedence of contextual dimensions for delivering mobile information to students. It involved delivering information via RSS and Twitter and surveying students about contexts like location, time of day, and activity. Preliminary results showed students prioritized location over other contexts. The ongoing study will analyze dimensions in more realistic situations and develop mobile apps for additional contexts.
APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN LAWN MEASUREMENT
Lawn area measurement is an application of image processing and deep learning. Researchers used
hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’
effectiveness and accuracy varies. In this project, deep learning method, specifically Convolutional neural
network, was applied to measure the lawn area. We used Keras and TensorFlow in Python to develop a
model that was trained on the dataset of houses then tuned the parameters with GridSearchCV in ScikitLearn (a machine learning library in Python) to estimate the lawn area. Convolutional neural network or
shortly CNN shows high accuracy (94 -97%). We may conclude that deep learning method, especially
CNN, could be a good method with a high state-of-art accuracy.
[Chung il kim] 0829 thesis
This document summarizes a presentation on using convolutional deep belief networks (CDBNs) for unsupervised feature learning from audio data. It describes CDBNs and how they are composed of convolutional restricted Boltzmann machines trained in a greedy layer-wise fashion. It then discusses how CDBNs were applied to unlabeled speech and music audio clips to learn hierarchical representations, which were then evaluated on speech recognition and music classification tasks. The results showed the CDBN-learned features outperformed raw audio features and MFCC features.
Introduction to Deep learning
My slides on 'Introduction to Deep learning' presented for the first
meeting of deep learning @ Leeds community.
Intepretability / Explainable AI for Deep Neural Networks
This document discusses interpretability and explainable AI (XAI) in neural networks. It begins by providing motivation for why explanations of neural network predictions are often required. It then provides an overview of different interpretability techniques, including visualizing learned weights and feature maps, attribution methods like class activation maps and guided backpropagation, and feature visualization. Specific examples and applications of each technique are described. The document serves as a guide to interpretability and explainability in deep learning models.
CNN Structure: From LeNet to ShuffleNet
This document summarizes the evolution of CNN architectures from LeNet to more recent models like ShuffleNet. It traces the development of techniques such as skip connections in ResNet to reduce information loss, depthwise separable convolutions in Xception to decouple spatial and channel correlations, group convolutions in ResNeXt to introduce cardinality as a new dimension, and channel shuffling in ShuffleNet to improve information flow across feature maps. The document highlights how these newer models have achieved state-of-the-art accuracy on ImageNet with increasingly efficient structures.
An Evolutionary-based Neural Network for Distinguishing between Genuine and P...
I presented this paper (https://doi.org/10.5220/0010985100003116) at 14th International Conference on Agents and Artificial Intelligence. It analyzes observers’ pupillary responses by developing neural network (NN) models to distinguish between genuine and posed anger.
Deep learning 1
The document provides an introduction to deep learning and discusses some of its key concepts and challenges. It motivates deep learning as a way to create algorithms that can understand scenes and describe them using natural language by automatically learning a series of abstractions from images to concepts. Some challenges discussed include modeling complex behavior with many concepts and interactions. Deep learning approaches like deep belief networks are presented as a way to learn multiple levels of representation from data with little supervision by first pre-training the networks in an unsupervised manner before fine-tuning them for the task.
Recommended
Exploring the Order of Precedence when Using Contextual Dimensions for Mobile...
This document summarizes a study exploring the order of precedence of contextual dimensions for delivering mobile information to students. It involved delivering information via RSS and Twitter and surveying students about contexts like location, time of day, and activity. Preliminary results showed students prioritized location over other contexts. The ongoing study will analyze dimensions in more realistic situations and develop mobile apps for additional contexts.
APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN LAWN MEASUREMENT
Lawn area measurement is an application of image processing and deep learning. Researchers used
hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’
effectiveness and accuracy varies. In this project, deep learning method, specifically Convolutional neural
network, was applied to measure the lawn area. We used Keras and TensorFlow in Python to develop a
model that was trained on the dataset of houses then tuned the parameters with GridSearchCV in ScikitLearn (a machine learning library in Python) to estimate the lawn area. Convolutional neural network or
shortly CNN shows high accuracy (94 -97%). We may conclude that deep learning method, especially
CNN, could be a good method with a high state-of-art accuracy.
[Chung il kim] 0829 thesis
This document summarizes a presentation on using convolutional deep belief networks (CDBNs) for unsupervised feature learning from audio data. It describes CDBNs and how they are composed of convolutional restricted Boltzmann machines trained in a greedy layer-wise fashion. It then discusses how CDBNs were applied to unlabeled speech and music audio clips to learn hierarchical representations, which were then evaluated on speech recognition and music classification tasks. The results showed the CDBN-learned features outperformed raw audio features and MFCC features.
Introduction to Deep learning
My slides on 'Introduction to Deep learning' presented for the first
meeting of deep learning @ Leeds community.
Intepretability / Explainable AI for Deep Neural Networks
This document discusses interpretability and explainable AI (XAI) in neural networks. It begins by providing motivation for why explanations of neural network predictions are often required. It then provides an overview of different interpretability techniques, including visualizing learned weights and feature maps, attribution methods like class activation maps and guided backpropagation, and feature visualization. Specific examples and applications of each technique are described. The document serves as a guide to interpretability and explainability in deep learning models.
CNN Structure: From LeNet to ShuffleNet
This document summarizes the evolution of CNN architectures from LeNet to more recent models like ShuffleNet. It traces the development of techniques such as skip connections in ResNet to reduce information loss, depthwise separable convolutions in Xception to decouple spatial and channel correlations, group convolutions in ResNeXt to introduce cardinality as a new dimension, and channel shuffling in ShuffleNet to improve information flow across feature maps. The document highlights how these newer models have achieved state-of-the-art accuracy on ImageNet with increasingly efficient structures.
An Evolutionary-based Neural Network for Distinguishing between Genuine and P...
I presented this paper (https://doi.org/10.5220/0010985100003116) at 14th International Conference on Agents and Artificial Intelligence. It analyzes observers’ pupillary responses by developing neural network (NN) models to distinguish between genuine and posed anger.
Deep learning 1
The document provides an introduction to deep learning and discusses some of its key concepts and challenges. It motivates deep learning as a way to create algorithms that can understand scenes and describe them using natural language by automatically learning a series of abstractions from images to concepts. Some challenges discussed include modeling complex behavior with many concepts and interactions. Deep learning approaches like deep belief networks are presented as a way to learn multiple levels of representation from data with little supervision by first pre-training the networks in an unsupervised manner before fine-tuning them for the task.
Deep randomized neural networks
The document summarizes research on deep randomized neural networks. It provides an overview of the field, discussing key concepts such as accuracy, complexity of models, and comparing deep randomized neural networks to other approaches like linear models and SVMs. It also reviews several papers that study properties of randomized neural networks, such as their intrinsic dimension and generalization capabilities. Various applications of randomized networks are explored, such as in classification and time series prediction tasks.
Community Analysis of Deep Networks (poster)
This document proposes research to analyze hidden unit collaboration and community formation in deep networks. It defines collaborating units as having high mutual information and communities as sets with greater within- than between-set collaboration. It poses leading questions on community existence, necessity, specialization, and abstraction. Preliminary experiments on MNIST and toy data trained stacked denoising autoencoders and measured community modularity and size over epochs, finding fluctuation that stabilizes with training. Future work is outlined on overlapping communities, evolution relations, layer/unit effects, overfitting, and learning algorithms.
Neural Networks and Deep Learning Syllabus
This document provides an overview of a 65-hour course on neural networks and deep learning taught by Andres Mendez Vazquez at Cinvestav Guadalajara. The course objectives are to introduce students to concepts of neural networks, with a focus on various neural network architectures and their applications. Topics covered include traditional neural networks, deep learning, optimization techniques for training deep models, and specific deep learning architectures like convolutional and recurrent neural networks. The course grades are based on midterms, homework assignments, and a final project.
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...
Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...Universitat Politècnica de Catalunya
This document summarizes techniques for interpreting convolutional neural networks (CNNs). It discusses visualizing learned weights, feature maps, and using attribution methods like class activation maps and gradient-based approaches to identify important regions of input for predictions. Feature visualization techniques are also covered, which generate examples to understand what patterns CNNs recognize. The document provides examples and references to papers for each interpretability method.Functional Brain Networks - Javier M. Buldù
This document discusses functional brain networks and network science approaches to studying the brain. It begins by defining complex systems and network science. It then outlines the main types of brain networks - anatomical and functional networks. Functional brain networks are constructed from time series data measuring brain activity and can be analyzed using network measures to study properties like segregation, integration and resilience.
Paper sharing_deep learning for smart manufacturing methods and applications
From Journal of Manufacturing Systems
Jinjiang Wang,Yulin Ma, Laibin Zhanga, Robert X. Gao, Dazhong Wu(2018)
neuronal network of NS systems short.ppt
Zhou Changsong presents a document discussing the brain as a complex dynamical network system subject to constraints of cost and function. It aims to reconcile irregular neuronal spiking with neural avalanches through a biologically plausible neuronal network model and statistical physics analysis. The key findings are that the model shows coexistence of irregular spiking, oscillations, and critical avalanches through a dynamical mechanism of Hopf bifurcation in the mean field model that explains critical neural avalanches corresponding to irregular spiking in the microscopic neuronal network model. This multiscale variability in brain activity reflects principles of cost-efficient neural representation and dynamics.
Details of Lazy Deep Learning for Images Recognition in ZZ Photo app
В докладе представлена тема глубокого обучения (Deep Learning) для распознавания изображений. Рассматриваются практические аспекты обучения глубоких сверточных сетей на GPU, обсуждается личный опыт портирования обученных нейросетей в приложение на основе библиотеки OpenCV, проводится сравнение полученного детектора домашних животных на основе подхода Lazy Deep Learning с детектором Виолы-Джонса.
Докладчики: Артем Чернодуб – эксперт в области искусственных нейронных сетей и систем искусственного интеллекта. В 2007 году закончил Московский физико-технический институт. Руководит направлением Computer Vision в компании ZZ Wolf, а также по совместительству работает научным сотрудником в Институте проблем математических машин и систем НАНУ.
Юрий Пащенко – специалист в области систем машинного зрения и машинного обучения, магистр НТУУ «Киевский Политехнический Институт», факультет прикладной математики (2014). Работает в компании ZZ Wolf на должности R&D Engineer.
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Артем Чернодуб (Computer Vision Team, ZZ Wolf)
"Распознавание изображений методом Lazy Deep Learning в фото-органайзере ZZ Photo"
В докладе рассматривается проблема распознавания изображений методами машинного зрения. Проводится краткий обзор существующих подзадач в этой области (детекция обьектов, классификация сцен, ассоциативный поиск в базах изображений, распознавание лиц и др.) и современных методов их решения с акцентом на глубокое обучение (Deep Learning).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Learning Sparse Neural Networksvia Sensitivity-Driven Regularization
The ever-increasing number of parameters in deep neural networks poses challengesfor memory-limited applications. Regularize-and-prune methods aim at meetingthese challenges by sparsifying the network weights. In this context we quantifythe outputsensitivityto the parameters (i.e. their relevance to the network output)and introduce a regularization term that gradually lowers the absolute value ofparameters with low sensitivity. Thus, a very large fraction of the parametersapproach zero and are eventually set to zero by simple thresholding. Our methodsurpasses most of the recent techniques both in terms of sparsity and error rates. Insome cases, the method reaches twice the sparsity obtained by other techniques atequal error rates.
Brain Networks
1. The document examines the relationship between brain anatomical networks and intelligence by analyzing structural, functional, and effective connectivity patterns.
2. It reviews concepts from graph theory and complex networks that are relevant for studying brain networks, including small-world networks and scale-free networks.
3. An experiment analyzed diffusion tensor images and other data from 79 subjects to construct and analyze anatomical brain networks and investigate their relationships with general and high intelligence.
A scoping review of Machine Learning in Seismic Geophysics
This document summarizes a scoping review of machine learning applications in seismic geophysics. It finds that machine learning, especially artificial neural networks and deep learning, has grown significantly in seismic data interpretation and processing tasks like rock property estimation, event picking, noise attenuation, and velocity analysis. Recent research has increasingly used convolutional neural networks and generative adversarial networks for seismic applications. Overall, the review concludes there has been an acute recent movement towards deep learning solutions for seismic data challenges posed by large, heterogeneous datasets.
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occam’s razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Presentation for "Iterative Multi-document Neural Attention for Multiple Answer Prediction" at AI*IA 2016 - URANIA 2016 Workshop (Genova).
See abstract here: http://lia.disi.unibo.it/Events/Confs&Works/URANIA2016/program.html
intrusion-detection-using-ML.pptx
An intrusion detection system uses machine learning techniques to classify network traffic as either normal or anomalous. Common machine learning approaches for anomaly detection include single classifiers like k-nearest neighbors, support vector machines, neural networks, and decision trees. Hybrid and ensemble classifiers can also be used, which combine the outputs of multiple classifiers. Choosing the right machine learning technique requires considering factors like the costs of false positives and negatives, diversity of network traffic, lack of training data, and the need to explain detections to humans.
AI alignment from the Active Inference perspective 2023.pdf
AI alignment from the perspective of Active Inference. The stack of "alignments": methodological is more important than scientific alignment which is more important than factual alignment, of which goal alignment is a specific type. However, there are also other, non-Bayesian perspectives on alignment that are also important to take, and alignment in and of itself is not enough to ensure that the AI transition goes well for humanity.
Evolution of Deep Learning and new advancements
Earlier known as neural networks, deep learning saw a remarkable resurgence in the past decade. Neural networks did not find enough adopters in the past century due to its limited accuracy in real world applications (due to various reasons) and difficult interpretation. Many of these limitations got resolved in the recent years, and it was re-branded as deep learning. Now deep learning is widely used in industry and has become a popular research topic in academia. Learning about the passage of its evolution and development is intriguing. In this presentation, we will learn about how we resolved the issues in last generation neural networks, how we reached to the recent advanced methods from the earlier works, and different components of deep learning models.
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020Universitat Politècnica de Catalunya
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.Multi sensor-fusion
The slide is written based on the first part of survey paper: W. Li, Z. Wang, G. Wei, L. Ma, J. Hu, and D. Ding, “A survey
on multi-sensor fusion and consensus filtering for sensor
networks,” Discrete Dynamics in Nature and Society, vol.
2015, Article ID 683701, 12 pages, 2015., see more in: http://dx.doi.org/10.1155/2015/683701.
Prismoid
PRISMOID is a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. It contains over 17,000 PTM sites from nearly 4,000 proteins annotated with 37 different types of PTMs. PRISMOID also annotates disease mutations affecting PTM sites. It collects protein structural features like secondary structure, solvent accessibility, and disorder regions. PRISMOID maps PTM sites from sequence databases to 3D protein structures from the PDB. It aims to provide an interactive resource for visualizing protein structures with PTMs and their associations with disease mutations.
Torsion Angles, ASA Used for prediction of Non - Enzymatic PTM
GlyStruct is a machine learning model that uses structural properties of amino acid residues to predict glycated and non-glycated lysine residues with improved 10% performance over existing methods. It extracts features including secondary structure, accessible surface area, and local backbone torsion angles from a dataset of 1753 lysine sites using the SPIDER2 toolbox. A support vector machine classifier is trained on a 104-dimensional feature vector constructed from a 13 amino acid segment window to predict glycation.
More Related Content
Similar to Cnn april 8 2020
Deep randomized neural networks
The document summarizes research on deep randomized neural networks. It provides an overview of the field, discussing key concepts such as accuracy, complexity of models, and comparing deep randomized neural networks to other approaches like linear models and SVMs. It also reviews several papers that study properties of randomized neural networks, such as their intrinsic dimension and generalization capabilities. Various applications of randomized networks are explored, such as in classification and time series prediction tasks.
Community Analysis of Deep Networks (poster)
This document proposes research to analyze hidden unit collaboration and community formation in deep networks. It defines collaborating units as having high mutual information and communities as sets with greater within- than between-set collaboration. It poses leading questions on community existence, necessity, specialization, and abstraction. Preliminary experiments on MNIST and toy data trained stacked denoising autoencoders and measured community modularity and size over epochs, finding fluctuation that stabilizes with training. Future work is outlined on overlapping communities, evolution relations, layer/unit effects, overfitting, and learning algorithms.
Neural Networks and Deep Learning Syllabus
This document provides an overview of a 65-hour course on neural networks and deep learning taught by Andres Mendez Vazquez at Cinvestav Guadalajara. The course objectives are to introduce students to concepts of neural networks, with a focus on various neural network architectures and their applications. Topics covered include traditional neural networks, deep learning, optimization techniques for training deep models, and specific deep learning architectures like convolutional and recurrent neural networks. The course grades are based on midterms, homework assignments, and a final project.
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...
Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...Universitat Politècnica de Catalunya
This document summarizes techniques for interpreting convolutional neural networks (CNNs). It discusses visualizing learned weights, feature maps, and using attribution methods like class activation maps and gradient-based approaches to identify important regions of input for predictions. Feature visualization techniques are also covered, which generate examples to understand what patterns CNNs recognize. The document provides examples and references to papers for each interpretability method.Functional Brain Networks - Javier M. Buldù
This document discusses functional brain networks and network science approaches to studying the brain. It begins by defining complex systems and network science. It then outlines the main types of brain networks - anatomical and functional networks. Functional brain networks are constructed from time series data measuring brain activity and can be analyzed using network measures to study properties like segregation, integration and resilience.
Paper sharing_deep learning for smart manufacturing methods and applications
From Journal of Manufacturing Systems
Jinjiang Wang,Yulin Ma, Laibin Zhanga, Robert X. Gao, Dazhong Wu(2018)
neuronal network of NS systems short.ppt
Zhou Changsong presents a document discussing the brain as a complex dynamical network system subject to constraints of cost and function. It aims to reconcile irregular neuronal spiking with neural avalanches through a biologically plausible neuronal network model and statistical physics analysis. The key findings are that the model shows coexistence of irregular spiking, oscillations, and critical avalanches through a dynamical mechanism of Hopf bifurcation in the mean field model that explains critical neural avalanches corresponding to irregular spiking in the microscopic neuronal network model. This multiscale variability in brain activity reflects principles of cost-efficient neural representation and dynamics.
Details of Lazy Deep Learning for Images Recognition in ZZ Photo app
В докладе представлена тема глубокого обучения (Deep Learning) для распознавания изображений. Рассматриваются практические аспекты обучения глубоких сверточных сетей на GPU, обсуждается личный опыт портирования обученных нейросетей в приложение на основе библиотеки OpenCV, проводится сравнение полученного детектора домашних животных на основе подхода Lazy Deep Learning с детектором Виолы-Джонса.
Докладчики: Артем Чернодуб – эксперт в области искусственных нейронных сетей и систем искусственного интеллекта. В 2007 году закончил Московский физико-технический институт. Руководит направлением Computer Vision в компании ZZ Wolf, а также по совместительству работает научным сотрудником в Институте проблем математических машин и систем НАНУ.
Юрий Пащенко – специалист в области систем машинного зрения и машинного обучения, магистр НТУУ «Киевский Политехнический Институт», факультет прикладной математики (2014). Работает в компании ZZ Wolf на должности R&D Engineer.
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
23.05.15 Одесса. Impact Hub Odessa. Конференция AI&BigData Lab
Артем Чернодуб (Computer Vision Team, ZZ Wolf)
"Распознавание изображений методом Lazy Deep Learning в фото-органайзере ZZ Photo"
В докладе рассматривается проблема распознавания изображений методами машинного зрения. Проводится краткий обзор существующих подзадач в этой области (детекция обьектов, классификация сцен, ассоциативный поиск в базах изображений, распознавание лиц и др.) и современных методов их решения с акцентом на глубокое обучение (Deep Learning).
Подробнее:
http://geekslab.co/
https://www.facebook.com/GeeksLab.co
https://www.youtube.com/user/GeeksLabVideo
Learning Sparse Neural Networksvia Sensitivity-Driven Regularization
The ever-increasing number of parameters in deep neural networks poses challengesfor memory-limited applications. Regularize-and-prune methods aim at meetingthese challenges by sparsifying the network weights. In this context we quantifythe outputsensitivityto the parameters (i.e. their relevance to the network output)and introduce a regularization term that gradually lowers the absolute value ofparameters with low sensitivity. Thus, a very large fraction of the parametersapproach zero and are eventually set to zero by simple thresholding. Our methodsurpasses most of the recent techniques both in terms of sparsity and error rates. Insome cases, the method reaches twice the sparsity obtained by other techniques atequal error rates.
Brain Networks
1. The document examines the relationship between brain anatomical networks and intelligence by analyzing structural, functional, and effective connectivity patterns.
2. It reviews concepts from graph theory and complex networks that are relevant for studying brain networks, including small-world networks and scale-free networks.
3. An experiment analyzed diffusion tensor images and other data from 79 subjects to construct and analyze anatomical brain networks and investigate their relationships with general and high intelligence.
A scoping review of Machine Learning in Seismic Geophysics
This document summarizes a scoping review of machine learning applications in seismic geophysics. It finds that machine learning, especially artificial neural networks and deep learning, has grown significantly in seismic data interpretation and processing tasks like rock property estimation, event picking, noise attenuation, and velocity analysis. Recent research has increasingly used convolutional neural networks and generative adversarial networks for seismic applications. Overall, the review concludes there has been an acute recent movement towards deep learning solutions for seismic data challenges posed by large, heterogeneous datasets.
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occam’s razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Presentation for "Iterative Multi-document Neural Attention for Multiple Answer Prediction" at AI*IA 2016 - URANIA 2016 Workshop (Genova).
See abstract here: http://lia.disi.unibo.it/Events/Confs&Works/URANIA2016/program.html
intrusion-detection-using-ML.pptx
An intrusion detection system uses machine learning techniques to classify network traffic as either normal or anomalous. Common machine learning approaches for anomaly detection include single classifiers like k-nearest neighbors, support vector machines, neural networks, and decision trees. Hybrid and ensemble classifiers can also be used, which combine the outputs of multiple classifiers. Choosing the right machine learning technique requires considering factors like the costs of false positives and negatives, diversity of network traffic, lack of training data, and the need to explain detections to humans.
AI alignment from the Active Inference perspective 2023.pdf
AI alignment from the perspective of Active Inference. The stack of "alignments": methodological is more important than scientific alignment which is more important than factual alignment, of which goal alignment is a specific type. However, there are also other, non-Bayesian perspectives on alignment that are also important to take, and alignment in and of itself is not enough to ensure that the AI transition goes well for humanity.
Evolution of Deep Learning and new advancements
Earlier known as neural networks, deep learning saw a remarkable resurgence in the past decade. Neural networks did not find enough adopters in the past century due to its limited accuracy in real world applications (due to various reasons) and difficult interpretation. Many of these limitations got resolved in the recent years, and it was re-branded as deep learning. Now deep learning is widely used in industry and has become a popular research topic in academia. Learning about the passage of its evolution and development is intriguing. In this presentation, we will learn about how we resolved the issues in last generation neural networks, how we reached to the recent advanced methods from the earlier works, and different components of deep learning models.
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020Universitat Politècnica de Catalunya
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.Multi sensor-fusion
The slide is written based on the first part of survey paper: W. Li, Z. Wang, G. Wei, L. Ma, J. Hu, and D. Ding, “A survey
on multi-sensor fusion and consensus filtering for sensor
networks,” Discrete Dynamics in Nature and Society, vol.
2015, Article ID 683701, 12 pages, 2015., see more in: http://dx.doi.org/10.1155/2015/683701.
Similar to Cnn april 8 2020 (20)
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Advanced Deep Architectures (D2L6 Deep Learning for Speech and Language UPC 2...
Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...
Interpretability of Convolutional Neural Networks - Xavier Giro - UPC Barcelo...
Paper sharing_deep learning for smart manufacturing methods and applications
Paper sharing_deep learning for smart manufacturing methods and applications
Details of Lazy Deep Learning for Images Recognition in ZZ Photo app
Details of Lazy Deep Learning for Images Recognition in ZZ Photo app
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
AI&BigData Lab. Артем Чернодуб "Распознавание изображений методом Lazy Deep ...
Learning Sparse Neural Networksvia Sensitivity-Driven Regularization
Learning Sparse Neural Networksvia Sensitivity-Driven Regularization
A scoping review of Machine Learning in Seismic Geophysics
A scoping review of Machine Learning in Seismic Geophysics
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Intuidex - To be or not to be iid by William M. Pottenger (NYC Machine Learni...
Iterative Multi-document Neural Attention for Multiple Answer Prediction
Iterative Multi-document Neural Attention for Multiple Answer Prediction
AI alignment from the Active Inference perspective 2023.pdf
AI alignment from the Active Inference perspective 2023.pdf
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020
Deep Learning Representations for All - Xavier Giro-i-Nieto - IRI Barcelona 2020
More from Subash Chandra Pakhrin
Prismoid
PRISMOID is a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. It contains over 17,000 PTM sites from nearly 4,000 proteins annotated with 37 different types of PTMs. PRISMOID also annotates disease mutations affecting PTM sites. It collects protein structural features like secondary structure, solvent accessibility, and disorder regions. PRISMOID maps PTM sites from sequence databases to 3D protein structures from the PDB. It aims to provide an interactive resource for visualizing protein structures with PTMs and their associations with disease mutations.
Torsion Angles, ASA Used for prediction of Non - Enzymatic PTM
GlyStruct is a machine learning model that uses structural properties of amino acid residues to predict glycated and non-glycated lysine residues with improved 10% performance over existing methods. It extracts features including secondary structure, accessible surface area, and local backbone torsion angles from a dataset of 1753 lysine sites using the SPIDER2 toolbox. A support vector machine classifier is trained on a 104-dimensional feature vector constructed from a 13 amino acid segment window to predict glycation.
COVID 19
This document summarizes recent research on COVID-19. It discusses the structure of coronaviruses and how they interact with the ACE2 receptor to infect cells. It also summarizes research using protein structure prediction, molecular modeling, and next generation sequencing to understand the virus and identify potential drug targets. Post-translational glycosylation of the spike protein is discussed and how it may help the virus evade immune detection.
Lstm covid 19 prediction
This presentation discusses using a Long Short Term Memory (LSTM) neural network model to predict global COVID-19 cases. It provides background on COVID-19 and reviews literature applying machine learning to predict disease spread. The methodology collects real-time case data, performs exploratory analysis, and trains an LSTM on 90% of normalized data. The model predicts daily cases for the held-out 10% and is evaluated on root mean square error. While the deep learning model captures some patterns, predictions are difficult given the pandemic's volatility. Social distancing remains important for health.
Rnn & Lstm
Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks can be used for sequence modeling tasks like predicting the next word. RNNs apply the same function to each element of a sequence but struggle with long-term dependencies. LSTMs address this with a gated cell that can maintain information over many time steps by optionally adding, removing, or updating cell state. LSTMs are better for tasks like language modeling since they can remember inputs from much earlier in the sequence. RNNs and LSTMs have applications in areas like music generation, machine translation, and predictive modeling.
Characterization and identification of lysine succinylation sites based
This document summarizes a research paper that uses deep learning methods to characterize and identify lysine succinylation sites in proteins. The researchers extracted four types of sequence-based features from protein data and used them to train convolutional neural networks (CNNs) for binary classification of succinylation sites versus non-sites. They evaluated ten-fold cross-validation of CNN models trained on different attribute combinations. The best performing model used position-specific scoring matrices and achieved an accuracy of 88%. However, the paper did not consider additional physicochemical properties that could improve predictions.
Deep Learning or Convolutional Neural Network
Convolutional neural networks (CNNs) are a type of neural network used in image recognition and processing. CNNs use convolutional layers that apply filters to input volumes to extract features at different spatial locations. Backpropagation is used to train CNNs by propagating errors backwards. CNNs have been successfully applied to large-scale image classification tasks using datasets like ImageNet, with AlexNet achieving breakthrough results in 2012. CNNs employ several convolutional layers interspersed with activation functions to process input volumes while avoiding excessive spatial shrinking.
Ncit 1st ai lab
This document summarizes three AI lab experiments:
1) A monkey and banana problem solved using Prolog code to represent states and moves to get the monkey from the floor to the banana hanging from the roof.
2) A water jug problem to get exactly 2 liters of water in a 4 liter jug using a pump and two jugs of 4 and 3 liters. States are represented as jug volumes and moves involve filling, pouring, and emptying jugs.
3) A farmer, wolf, goat, and cabbage river crossing puzzle where the farmer must transport all items across the river in a boat that holds himself and one other item, avoiding unsafe states like leaving the wolf and goat alone.
Ai lab
The monkey is on the floor by the door, a block is on the floor by the window, and a banana is hanging from the roof in the middle of the room. The problem presented is how the monkey can get the banana.
Analysis
The document discusses two modern techniques for determining system requirements: Joint Application Design (JAD) and prototyping. JAD involves collaborative workshops between users, analysts, and other stakeholders to jointly design systems. It leads to shorter development times and greater user satisfaction. Prototyping allows quickly building preliminary versions of a system to gather more requirements from user testing and feedback. Both techniques can effectively gather requirements while reducing analysis time compared to traditional methods.
Planning
This document discusses the process of identifying and selecting information systems development projects. It begins by outlining the three primary activities of project identification, classification and ranking, and selection. It then provides details on each step, including how potential projects are identified, common criteria for evaluating projects, and factors to consider when selecting projects. The deliverables are identified as a schedule of selected IS development projects. The outcomes include ensuring careful consideration was given to project selection and understanding how each project could help the organization.
Intelligent agents (bsc csit) lec 2
This document provides an overview of intelligent agents. It defines key concepts such as agents, environments, rational agents, and bounded rationality. It discusses different types of agent architectures including reactive agents, state-based agents, deliberative agents, utility-based agents, and learning agents. The document also describes different types of environments based on their observability, determinism, episodicity, dynamism, continuity, and whether they involve single or multiple agents. Overall, the document aims to familiarize readers with intelligent agents and their components and characteristics.
Final slide4 (bsc csit) chapter 4
The document describes various search algorithms used to solve pathfinding problems represented as graphs. It discusses uninformed searches like breadth-first search (BFS) and depth-first search (DFS) as well as methods for evaluating search performance in terms of completeness, time complexity, space complexity, and optimality. BFS uses a queue to expand shallow nodes first, guaranteeing an optimal solution but with exponential time and space costs. DFS uses a stack to expand deep nodes first, providing better space efficiency but not guaranteeing an optimal solution.
Final slide (bsc csit) chapter 5
The document discusses various informed search algorithms including A*, greedy search, and uniform cost search. It provides instructional objectives for learning about heuristic functions, designing heuristics for problems, and comparing heuristic functions. Key aspects of A* search are summarized, including that it uses an admissible heuristic function to find optimal solutions, and conditions like admissibility and consistency that guarantee its optimality.
Lec 6 bsc csit
Hill climbing is a local search algorithm that starts with a random solution and iteratively makes small changes to improve the solution. It terminates when no further improvements can be made. Hill climbing can get stuck at local optima rather than finding the global optimum. Simulated annealing is similar to hill climbing but allows occasional "downhill moves" that worsen the solution based on a probability function involving the change in solution quality and temperature parameter. The temperature is gradually decreased, reducing the probability of downhill moves over time. This helps simulated annealing avoid local optima and find better solutions than hill climbing.
Two player games
Two player games involve two players alternating turns trying to maximize or minimize the outcome of a game. The MiniMax algorithm is commonly used to choose the best move for a player in two player games by recursively evaluating all possible future moves. Alpha-beta pruning improves upon MiniMax by pruning branches that cannot influence the final outcome, allowing deeper search of the game tree within the same time limit.
Knnowledge representation and logic lec 11 to lec 15
FOL
Resolution in FOL
Predicate Logic
Truth table
Quantifiers
Quantified statement
Knowledge Base
Reasoning
Inference
Final slide (bsc csit) chapter 2
This document discusses different types of intelligent agents and their architectures. It defines agents as entities that operate in an environment, perceiving it through sensors and acting upon it through effectors to achieve their goals. The document outlines stimulus-response, state-based, deliberative, utility-based, and learning agent architectures. It also discusses concepts like rational agents, bounded rationality, and PEAS (Performance, Environment, Actuators, Sensors) representations for defining agent tasks.
Final slide (bsc csit) chapter 3
The document provides an introduction to state space search problems and algorithms. It discusses key concepts like the state space representation, initial and goal states, actions/operators that transform states, and different search strategies. Specific examples covered include the vacuums world problem, towers of Hanoi, water jugs problem, and the 8 queens puzzle. The document also introduces production systems and how they can be used to represent state space search problems.
More from Subash Chandra Pakhrin (20)
Torsion Angles, ASA Used for prediction of Non - Enzymatic PTM
Torsion Angles, ASA Used for prediction of Non - Enzymatic PTM
Characterization and identification of lysine succinylation sites based
Characterization and identification of lysine succinylation sites based
Knnowledge representation and logic lec 11 to lec 15
Knnowledge representation and logic lec 11 to lec 15
Recently uploaded
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective UpskillingExcellence Foundation for South Sudan
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS Template 2023-2024 by: Irene S. Rueco
Digital Artifact 1 - 10VCD Environments Unit
Digital Artifact 1 - 10VCD Environments Unit - NGV Pavilion Concept Design
Advanced Java[Extra Concepts, Not Difficult].docx
This is part 2 of my Java Learning Journey. This contains Hashing, ArrayList, LinkedList, Date and Time Classes, Calendar Class and more.
How to Build a Module in Odoo 17 Using the Scaffold Method
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)Academy of Science of South Africa
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Recently uploaded (20)
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Your Skill Boost Masterclass: Strategies for Effective Upskilling
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...
South African Journal of Science: Writing with integrity workshop (2024)
South African Journal of Science: Writing with integrity workshop (2024)
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Cnn april 8 2020
- 1. Convolutional Neural Network Subash Chandra Pakhrin PhD Student Wichita State University Wichita, Kansas
- 2. Convolutional Neural Network [1] Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
- 3. Back-Propagation [2] Rumelhart, D., Hinton, G. & Williams, R. Learning representations by back-propagating errors. Nature 323, 533–536 (1986). https://doi.org/10.1038/323533a0
- 4. Image Net Classification with Deep Convolutional Neural Networks [3] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, Image Net Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25 (NIPS 2012)
- 8. 7x7 input(spatially) assume 3x3 filter (Stride 1) 5 x 5 output i.e. 5 spatial location horizontally, 5 spatial location vertically
- 9. 7x7 input(spatially) assume 3x3 filter (Stride 2) 3 x 3 output i.e. 3 spatial location horizontally, 3 spatial location vertically
- 10. A closer look at spatial dimensions: • We don’t do convolution like this because it produces asymmetric output 7x7 input (spatially) assumes 3x3 filter applied with stride 3? Doesn’t fit!!! Cannot apply 3x3 filter on 7x7 input with stride 3.
- 12. In practice: Common to zero pad the border e.g. input 7x7 3x3 filter, applied with stride 1 Pad with 1 pixel border => what is the output? Output => 7x7 !!! (recall:) (N-F)/stride +1
- 13. Example: Input volume: 32x32x3 10, 5x5 filters with stride 1, pad 2 New N = 36 after pad 2, F = 5, stride = 1 (N-F)/stride +1 Output volume size:? 32 x 32 x 10 Number of parameters in this layer? each filter has 5*5*3+1 = 76 parameters (+1 for bias) =>76*10=760
- 14. Convolution Layer For example, if we had 6 5x5 filters, we’ll get 6 separate activation maps.
- 15. ConvNet is a sequence of Convolutional Layers, interspersed with activation functions Caution!!! 32x32 input convolved repeatedly with 5x5 filters shrinks volumes spatially! Shrinking too fast is not good, doesn’t work well.
- 17. References: [1] Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998 [2] Rumelhart, D., Hinton, G. & Williams, R. Learning representations by back-propagating errors. Nature 323, 533–536 (1986) [3] Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, Image Net Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25 (NIPS 2012) [4] Fei-Fei Li, Justin Johnson & Serena Yeung, Lecture 5| Convolutional Neural Networks, https://youtu.be/bNb2fEVKeEo [5] Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems 28 (NIPS 2015) [6] Daniel Levy, Arzav Jain, Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks, Computer Vision and Pattern Recognition, NIPS 2016 [7] Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei, A Hierarchical Approach for Generating Descriptive Image Paragraphs The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 317-325