a survey on deep learning: algorithms, techniques, and applications

The MNIST database of handwritten digits. CoRR abs/1602.05629 (2016). 1986. Like, collaborative filtering wants huge dataset with lively customers who valued a product before in order to create precise predictions. These ANNs constantly take learning algorithms and by continuously increasing the amounts of data, the efficiency of training processes can be improved. With the development of deep learning , the aggregate of pc imaginative and prescient and natural language device has aroused great interest within the beyond few years. The vast description technology method of excessive degree image semantics calls for now not handiest popularity of the item and the scene, however the ability of reading the dominion, the attributes and the connection among the ones devices. Evolving large-scale neural networks for vision-based reinforcement learning. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. IEEE, 1--6. 2014. 1996. 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Condition Monitoring of Power Insulators Using Intelligent Techniques - A Survey. A survey on deep learning in medical image analysis. Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2017. A Survey on Deep Learning: Algorithms, Techniques, and Applications, All Holdings within the ACM Digital Library. IEEE, 580--587. In 12th Annual Conference of the International Speech Communication Association. For example, it can automate feature engineering, it is easy to adapt to different fields and applications. In IEEE Conference on Computer Vision and Pattern Recognition. 2012. Ruslan Salakhutdinov and Geoffrey Hinton. 2015. 2016. Need more dimensions in your analyses and visualizations? Retrieved from http://arxiv.org/abs/1512.05193. IEEE Computer Society, 779--788. 2016. 2015. Deep learning is an emerging research area in machine learning and pattern recognition field. 2014. 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With growing popularity of social media and the anonymity and convenience it offers, has led to increase in hate speech, therefore, there is an urgent need for effective solution or countermeasures to tackle this problem In my paper, I have performed sentiment/emotion analysis on audio and recognize various emotions such as happy, sad, calm, angry etc. Deep belief net learning in a long-range vision system for autonomous off-road driving. In Empirical Methods in Natural Language Processing, Vol. UCF101: A dataset of 101 human actions classes from videos in the wild. 2011. Hsin-Yu Ha, Yimin Yang, Samira Pouyanfar, Haiman Tian, and Shu-Ching Chen. Citeseer, Association for Computational Linguistics, 31--39. Since most hearing people do not know how to "speak" the sign language and have little patience to practice, interacting with them is generally inconvenient for deaf people. Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, and Trey Ideker. 2016. Retrieved from http://arxiv.org/abs/1605.02688. Timothy Dozat. 2016. In International Conference on Machine Learning. 2017. IEEE, 50--57. 2012. Diederik P. Kingma and Max Welling. Xem v ti ngay bn y ca ti liu ti y (0 B, 36 trang ). Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. Deep learning techniques are such kinds of learning techniques that have more levels of representation and at a more conceptual level. 2. Accessed April 18, 2017. 2016. Apparent age estimation via human face image has attracted increased attention due to its numerous real-world applications. 2014. In Machine Learning in Health Care. Adam: A method for stochastic optimization. Traffic matrix prediction and estimation based on deep learning for data center networks. Learning fast approximations of sparse coding. 2014. More specifically, an error in a computer vision system of an autonomous car could lead to a crash, while in the medical area, human lives are depending on these decisions. Although sign language makes it easier for them to communicate with each other it also establishes a barrier between deaf and dumb individuals and ordinary individuals. 1724--1734. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. Anastasia Ioannidou, Elisavet Chatzilari, Spiros Nikolopoulos, and Ioannis Kompatsiaris. 2016. IEEE Computer Society, 3626--3633. Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, and Jakob Uszkoreit. . Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically study the hierarchical relationship in deep architectures for classification. 2016. John C. Duchi, Elad Hazan, and Yoram Singer. 2015. IEEE, 483--488. David G. Lowe. Grigorios Tsagkatakis, Mustafa Jaber, and Panagiotis Tsakalides. Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2015. | 2013. Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, and Paris Smaragdis. 2010. Nature Immunology 17, 8 (2016), 890--895. 2016. 2018. Retrieved from http://arxiv.org/abs/1706.05137. International Journal of Semantic Computing 11, 1 (2017), 85--109. 1999. Pattern Recognition 44, 3 (2011), 572--587. Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. This paper mainly adopts the summary and the induction methods of deep learning. Retrieved from https://deeplearning4j.org. 2015. Deep learning methods have made a significant breakthrough which can be appreciable performance in a wide variety of applications with useful security tools. Herbert Jaeger and Harald Haas. CoRR abs/1603.04467 (2016). The emphasis is on discussing the various A Survey on Deep Learning: Algorithms, Techniques, and Applications . CIFAR-10 and CIFAR-100 datasets. Inceptionism: Going deeper into neural networks. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. Modeling natural images using gated MRFs. 2017. A number of techniques came into existence to detect the intrusions on the basis of machine learning and deep learning procedures. Understanding human language: Can NLP and deep learning help? 1986. 2013. APSIPA Transactions on Signal and Information Processing 3 (2014), 1--29. Springer, 21--37. Will Kay, Joo Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. 2016. Retrieved from http://arxiv.org/abs/1603.04467. Curran Associates, 2672--2680. A deep learning technology works on the artificial neural network system (ANNs). Nature 538, 7623 (2016), 20--23. Nature 521, 7553 (2015), 436--444. Antonio Torralba, Rob Fergus, and William T. Freeman. This motivated me to perform sentiment analysis and hate speech detection on such a dynamic corpus amount of data available out there. A hierarchical model for device placement. A general survey ( Miotto et al., 2018) has been published, which covered deep learning diagnosis methods for different diseases. Kazuhiro Negi, Keisuke Dohi, Yuichiro Shibata, and Kiyoshi Oguri. ADADELTA: An adaptive learning rate method. 2017. Quoc V. Le. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Design by 123DOC, Xem v ti ngay bn y ca ti liu ti y (0 B, 36 trang ), bo co sinh hc:" Analysis of a survey on young doctors'''' willingness to work in rural Hungary pptx, Bo co ha hc: " A survey on biometric cryptosystems and cancelable biometrics" docx, a survey on parent' attiudes towards their children's learning english at the primary school = b kho st thi ca ph huynh v vic hc ting anh ca con trng tiu hc, A survey on the first year students English language learning style preferences at Hanoi University of Business and Technology = Kho st phong cch hc tin, A survey on the Perception of students at Thanh Liem a high school of the benefits of Fre-listening activities in learning listening skills = Nghin cu kho s, A SURVEY ON STUDENTS ATTITUDES TOWARDS LEARNING SPEAKING SKILLS AT UNIVERSITY OF SOCIAL SCIENCES AND HUMANITIES, VNU, A survey on teaching and learning English reading skill at Vietnam Maritime University Kho st tnh hnh dy v hc k nng c hiu ting Anh ti i hc Hng Hi Vit Nam, Industry 4.0: A Survey on Technologies, Applications and Open Research Issues, A SURVEY ON THE USE MODEL ESSAYS IN LEARNING ESSAY WRITING OF ENGLISH MAJOR STUDENTS AT CTU. Retrieved from http://arxiv.org/abs/1609.08675. ISCA. 2013. One model to learn them all. In IEEE International Conference on Neural Networks, Vol. Association for Computer Linguistics, 1--18. Minwei Feng, Bing Xiang, Michael R. Glass, Lidan Wang, and Bowen Zhou. However, the survey does not provide a detailed experimental results comparison of all the proposed methods. Retrieved from http://arxiv.org/abs/1602.05629. Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry P. Heck. Fully convolutional networks for semantic segmentation. The task of Image captioning needs to evaluate an image, with respect to the subjects and objects in the image, the relationship between these semantic details needs to be determined accurately along with other attributes and features present in the image. 2013. Recent and upcoming trends in the field of artificial intelligence (AI) and its categories have been emphasized and potential challenges have been discussed. Xueliang Zhang and DeLiang Wang. Rasool Fakoor, Faisal Ladhak, Azade Nazi, and Manfred Huber. 2015. 2017. Accessed April 18, 2017. Matthew D. Zeiler. Michel Lang, Helena Kotthaus, Peter Marwedel, Claus Weihs, Jrg Rahnenfhrer, and Bernd Bischl. 2006. 2016. Deep learning. 2014. Proceedings of the IEEE 86, 11 (1998), 2278--2324. Divya Sunny. In International Conference on Machine Learning, Maria Florina Balcan and Kilian Q. Weinberger (Eds. IEEE, 268--275. Deep learning-based methods are responsible for controlling the complexities and challenges of image recognition 1U.G. 2017. Earlier methods were used to predict breast cancer diagnoses like data mining techniques, machine learning techniques, and the hybrid form of data mining and machine learning systems with a comparison of their accuracy. In IEEE Conference on Computer Vision and Pattern Recognition. CoRR abs/1706.00612 (2017). The rapid development of computer-based research, methods, and applications to replicate human intelligence is called artificial intelligence (AI). In the recent years it, 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). 2014. cuDNN: Efficient primitives for deep learning. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. Rich feature hierarchies for accurate object detection and semantic segmentation. In The 13th International Conference on Artificial Intelligence and Statistics, Vol. We have seen fewdeep learning methods rooted from initialANNs, including DBNs, RBMs, RNNs, ConvolutionalNeural Networks (CNNs) [77, 86]. Neural Computation 18, 7 (July 2006), 1527--1554. MIT Press. In IEEE International Conference on Acoustics, Speech and Signal Processing. Learning deconvolution network for semantic segmentation. 2015. Retrieved from https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html. In European Conference on Computer Vision. In International Conference on Learning Representations Workshop. In International Workshop on Machine Learning in Medical Imaging. Florida International University, Miami, FL. Ting Chen and Christophe Chefdhotel. Retrieved from http://arxiv.org/abs/1703.09452. Springer, 17--24. Adam Coates, Brody Huval, Tao Wang, David J. Wu, Andrew Y. Ng, and Bryan Catanzaro. CoRR abs/1602.07563 (2016). Automatic video event detection for imbalance data using enhanced ensemble deep learning. Deep learning based automatic immune cell detection for immunohistochemistry images. 2015. Navneet Dalal and Bill Triggs. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger. In The 10th International Workshop on Semantic Evaluation. Neural networks for continuous online learning and control. Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning methods are data-driven and need a lot of data to train the model, while the methods of traditional machine learning only need a relatively small amount of data. Deep residual learning for image recognition. Retrieved from https://www.nervanasys.com/technology/neon. of development, deep learning now is one of the most efficient tools compared to other machinelearning algorithms with great performance. Object recognition from local scale-invariant features. 2016. In IEEE International Conference on Acoustics, Speech and Signal Processing. This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. Association for Computational Linguistics, 340--348. Kyunghyun Cho, Bart van Merrienboer, aglar Glehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. A fast learning algorithm for deep belief nets. NEW NLP driven algorithms behind Create Concept Grid (for terms) and Cluster Records (for records) automate the clustering, naming, and visualization of a topic's major areas and underlying sub-areas, all while maintaining detail drill-down ability. Retrieved from http://arxiv.org/abs/1409.1556. In IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1562--1566. Retrieved from http://trecvid.nist.gov. Geoffrey E. Hinton. Faster R-CNN: Towards real-time object detection with region proposal networks. Springer, 473--487. 2004. . IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 11 (2008), 1958--1970. demand response program: 2013. Hayit Greenspan, Bram van Ginneken, and Ronald M. Summers. 2013. Deep stacking networks for information retrieval. Retrieved from http://arxiv.org/abs/1602.07563. A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. arxiv:1703.09452. Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. Large-scale transportation network congestion evolution prediction using deep learning theory. Proponents included Allen Newell, Herbert A. Simon, and Marvin Minsky. One of the foremost common types of cancer is breast cancer and early prediction and diagnosis avoid the rising number of deaths. In this paper, we present a survey on the several methods adopted for this task. MNIST. IEEE/ACM Transactions on Audio, Speech and Language Processing 25, 1 (2017), 153--167. 1962. Over the course of the last decade, Deep Learning and Artificial Intelligence (AI) became the main technologies behind many breakthroughs in computer vision [Krizhevsky et al., 2012], robotics [Andrychowicz et al., 2018]and Natural Language Processing (NLP) [Goldberg, 2017].They also have a major impact in the autonomous driving revolution seen today both in academia and industry. This paper gives an audit of 40 noteworthy works that covers the period from 2015 to 2019. Using deep learning to enhance cancer diagnosis and classification. Convolutional networks for images, speech, and time series. Geert Litjens, Clara I. Snchez, Nadya Timofeeva, Meyke Hermsen, Iris Nagtegaal, Iringo Kovacs, Christina Hulsbergen-Van De Kaa, Peter Bult, Bram Van Ginneken, and Jeroen Van Der Laak. Link prediction in social networks: The state-of-the-art. 1. Fast R-CNN. This survey provides a comprehensive analysis of DRL and different types of neural network, DRL architectures, and their real-world applications. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. In European Conference on Computer Vision. Copyright 2022 ACM, Inc. Martn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Man, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vigas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 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Deep learning uses multiple layers to represent the abstractions of data to build computational models. Omnipress. Christof Angermueller, Tanel Prnamaa, Leopold Parts, and Oliver Stegle. LVM can be generally divided into statistical learning-based classic LVM and neural networks-based deep . 2014. ACM, 1041--1044. In Advances in Neural Information Processing Systems. 53 PDF View 1 excerpt, cites background A deep learning prediction process accelerator based FPGA. Warren S. McCulloch and Walter Pitts. 2017. 2012. Efficient imbalanced multimedia concept retrieval by deep learning on spark clusters. Convolutional two-stream network fusion for video action recognition. CoRR abs/1605.02688 (2016). 2004. 2017. Jean-Claude Junqua and Jean-Paul Haton. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect-oriented product analysis, sentiment analysis and text classification like email categorization and spam filtering. In IEEE Workshop on Automatic Speech Recognition and Understanding. Table 1: Image augmentation algorithms used in those papers related to image classification and object detection. 1943. SEGAN: Speech enhancement generative adversarial network. Communications of the ACM 59, 2 (2016), 64--73. Deep Learning, Vol. Department of Computer and Information Science and Engineering. 2009. This work proposes two approaches to dependency parsing especially for languages with restricted amount of training data and suggests that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. Deep learning is an effective and useful technique that has been widely applied in a variety of fields, including computer vision, machine vision, and natural language processing. In 2nd Workshop on Continuous Vector Space Models and their Compositionality. Yann LeCun, Lon Bottou, Yoshua Bengio, and Patrick Haffner. Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Alec Radford, Luke Metz, and Soumith Chintala. CoRR abs/1701.06420 (2017). Chao Wang, Lei Gong, Qi Yu, Xi Li, Yuan Xie, and Xuehai Zhou. Deep reinforcement learning: An overview. Deep neural networks for single-channel multi-talker speech recognition. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Omry Yadan, Keith Adams, Yaniv Taigman, and MarcAurelio Ranzato. 2015. 2015. Accessed April 18, 2017. Predicting the apparent age has been quite difficult for machines and humans. Statistical modeling: The two cultures. Robustness in Automatic Speech Recognition: Fundamentals and Applications, Vol. Deep Learning: Design and development of an intent-based intelligent network using machine learning for QoS provisioning. CoRR abs/1212.5701 (2012). 2015. Recurrent convolutional neural networks for speech processing. Deep pipelined one-chip FPGA implementation of a real-time image-based human detection algorithm. Florida International University Lin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-30 with Computers categories. MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems. 2017. "Deep learning is the most effective, supervised, time and cost efficient machine learning approach." According to A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning, one of the more widely cited research papers in the topic of deep learning applications, found here. 2017. 2009. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. 2010. CoRR abs/1312.6114 (2013). Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frdric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brbisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Ct, Myriam Ct, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mlanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balzs Hidasi, Sina Honari, Arjun Jain, Sbastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, Csar Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Lonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merrinboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, Franois Savard, Jan Schlter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, tienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jrmie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, and Ying Zhang. Communication is one of the most important things when it comes to living in a society, well, average people do not suffer much because they are able to interact properly with those around them. Deep Boltzmann machines. The efficiency is dependent on the larger data volumes. Idiap. 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