Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. (2015a), Shi et al. LSTM is based on recurrent network along with gradient-based learning algorithm (Hochreiter and Schmidhuber, 1997) LSTM introduced self-loops to produce paths so that gradient can flow (Goodfellow et al., 2016). Restricted and Unrestricted Boltzmann Machines and their variants, Deep Boltzmann Machines, Deep Belief Networks (DBN), Directed Generative Nets, and Generative Stochastic Networks etc. Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, He has spoken and written a lot about what deep learning is and is a good place to start. (2015) presented a nice overview on recent advances of CNNs, multiple variants of CNN, its architectures, regularization methods and functionality, and applications in various fields. scaling algorithms for larger models and data, reducing optimization difficulties, designing efficient scaling methods etc. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, and their variants. (2016) proposed another VDCNN architecture for text classification which uses small convolutions and pooling. latent variables and one layer of observable variables (Deng and Yu (2014), Goodfellow et al. Resnet in resnet: Generalizing residual architectures. Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, and Zhen Wang. (2015), Liu et al. Schmidhuber (2014), Bengio (2009), Deng and Yu (2014), Goodfellow et al. Tang et al. Keeping up with the trend of many recent years, Deep Learning in 2020 continued to be one of the fastest-growing fields, darting straight ahead into the Future of Work. Advances in Quantum Deep Learning: An Overview Siddhant Garg*, Goutham Ramakrishnan* arXiv preprint - May 2020 *Equal contribution. Jürgen Schmidhuber. Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Larsson et al. Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Goodfellow et al. (2017)), Dota2 (Batsford (2014)), Atari (Mnih et al. Article link: https://www.researchgate.net/publication/323143191_Recent_Advances_in_Deep_Learning_An_Overview, https://www.researchgate.net/publication/323143191_Recent_Advances_in_Deep_Learning_An_Overview, Cats and Dogs classification using AlexNet, Finally, An Answer To Why So Many People Voted For Trump, The Modern World Has Finally Become Too Complex for Any of Us to Understand, How to Reverse Diabetes and Lose Belly Fat in 60 Days, What Science Says About Vitamins and Supplements for Covid-19, image classification and recognition (Simonyan and Zisserman (2014b), Krizhevsky et al. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. Faster R-CNN: Towards real-time object detection with region (2015) proposed a Deep Generative Model (DGM) called Laplacian Generative Adversarial Networks (LAPGAN) using Generative Adversarial Networks (GAN) approach. compositionality. Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, This means we don't have a direct analogy to the notion of some unique set of weights that perform well on the task at hand. To learn complicated functions, deep architectures are used with multiple levels of abstractions i.e. Li (2017) discussed Deep Reinforcement Learning(DRL), its architectures e.g. Advances in Quantum Deep Learning: An Overview. Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, (2014), Bahdanau et al. neural networks. Dally, and Kurt Keutzer. Sabour et al. Antonoglou, Daan Wierstra, and Martin A. Riedmiller. Iandola et al. https://doi.org/10.1109/ICIP.2013.6738559. Recent trends in deep learning based natural language processing. The recent advances reported for this task have been showing that deep learning is the most successful machine learning … Jason Weston, Sumit Chopra, and Antoine Bordes. 05/08/2020 ∙ by Siddhant Garg, et al. Zhang et al. Very deep convolutional networks for large-scale image recognition. Reinforcement learning uses reward and punishment system for the next move generated by the learning model. First generation of ANNs was composed of simple neural layers for Perceptron. The network composed of five convolutional layers and three fully connected layers. ∙ (2016) proposed ResNeXt architecture. Goodfellow et al. It is often hard to keep track with contemporary advances in a research area, provided that field has great value in near future and related applications. A deep learning framework for character motion synthesis and editing. They also mentioned optimization and future research of neural networks. Input, Question, Episodic Memory, Output (Kumar et al., 2015). (2013) proposed Maxout, a new activation function to be used with Dropout (Srivastava et al., 2014). We also explore the history of influence of physics in machine learning that is oft neglected in the Computer Science community, and how recent insights from physics hold the promise of opening the black box of deep learning. They claimed to achieve state-of-the-art in language understanding, better than other RNNs. 0 published a overview of Deep Learning (DL) models with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). DMN has four modules i.e. It is also one of the most popular scientific research trends now-a-days. Moniz and Pal (2016) proposed Convolutional Residual Memory Networks, which incorporates memory mechanism into Convolutional Neural Networks (CNN). We tested this agent on the challenging domain of … Playing atari with deep reinforcement learning. (2016), Variational Auto-Encoders (VAE) can be counted as decoders (Wang, ), . (2016) proposed ResNeXt architecture. 2.1 Recent advances Automated skin cancer detection is a challenging task due to the variability of skin lesions in the dermatology field. Huang et al. Also, Deep Learning (DL) models are immensely successful in Unsupervised, Hybrid and Reinforcement Learning as well. Also, previous papers focus from different perspectives. proposed batch-normalized LSTM (BN-LSTM), which uses batch-normalizing on hidden states of recurrent neural networks. Jürgen Schmidhuber. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang (2015) proposed Conditional Random Fields as Recurrent Neural Networks (CRF-RNN), which combines the Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for probabilistic graphical modelling. Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy He also discussed deep neural networks and deep learning to some extent. (2016) wrote and skillfully explained about Deep Feedforward Networks, Convolutional Networks, Recurrent and Recursive Networks and their improvements. Distributed representations of sentences and documents. The story for modern day deep learning optimizers started with vanilla gradient descent. Deng and Yu (2014) briefed deep architectures for unsupervised learning and explained deep Autoencoders in detail. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. (2016) proposed Fractal Networks i.e. ∙ A fast learning algorithm for image segmentation with max-pooling Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Then Support Vector Machine (SVM) surfaced, and surpassed ANNs for a while. Girshick (2015) proposed Fast Region-based Convolutional Network (Fast R-CNN). Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael (2015b), Zhang et al. http://proceedings.mlr.press/v37/ioffe15.html. (2016b) proposed Deep Long Short-Term Memory (DLSTM), which is a stack of LSTM units for feature mapping to learn representations (Shi et al., 2016b). Also, Deep Learning (DL) models are immensely successful in Unsupervised, Hybrid and Reinforcement Learning as well (LeCun et al., 2015). Autoencoders (AE) are neural networks (NN) where outputs are the inputs. (2015) proposed a DRL architecture using deep neural network (DNN). ∙ 0 ∙ share . By David Talby. In this section, we will discuss the main recent Deep Learning (DL) approaches derived from Machine Learning and brief evolution of Artificial Neural Networks (ANN), which is the most common form used for deep learning. (2017)), sentence modelling (Kalchbrenner et al., 2014), document and sentence processing (Le and Mikolov (2014), Mikolov et al. Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Bengio (2013) did quick overview on DL algorithms i.e. This paper provides a complete overview of the common deep learning frameworks used in sentiment analysis in recent time. (2013),Mnih et al. understanding. Generating sequences with recurrent neural networks. (2015), van Hasselt et al. to name a few. The briefed the models graphically along with the breakthroughs in DL research. Jürgen Schmidhuber. van Hasselt et al. Haohan Wang, Bhiksha Raj, and Eric P. Xing. The architecture used Graphics Processing Units (GPU) for convolution operation, Rectified Linear Units (ReLU) as activation function and Dropout. Feedforward Neural Networks (FNN), Convolutional Neural Netowrks (CNN), Recurrent Neural Networks (RNN) etc. Convolutional Neural Networks (CNN), Auto-Encoders (AE) etc. Gehring et al. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. Deep Q-Network (DQN), and applications in various fields. (2011), Redmon et al. (2014) proposed Generative Adversarial Nets (GAN) for estimating gen- erative models with an adversarial process. (2011) built a deep generative model using Deep Belief Network (DBN) for images recognition. There were many overview papers on Deep Learning (DL) in the past years. (2017) provided large-scale analysis of Vanilla LSTM and eight LSTM vari- ants for three uses i.e. neural networks into compressed and smaller model. Max-Pooling Convolutional Neural Networks (MPCNN) operate on mainly convolutions and max-pooling, especially used in digital image processing. Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja and Josef Urban. Read writing about Deep Learning in Recent Advances in Deep Learning: An Overview. This course aims to provide an overview of the recent developments in RL combined with advances in deep learning. Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. Deng (2011) gave an overview of deep structured learning and its architectures from the perspectives of information processing and related fields. Max-pooling layers down-sample images and keep the maximum value of a sub-region. Zisserman, 2014a), human action recognition (Ji et al., 2013), classifying and visualizing motion capture sequences (Cho and Chen, 2013), handwriting generation and prediction (Carter et al., 2016), automated and machine translation (Wu et al. 05/08/2020 ∙ by Siddhant Garg, et al. Szegedy et al. The auxiliary variables make variational distribution with stochastic layers and skip connections (Maaløe et al., 2016). It uses layers of capsules instead of layers of neurons, where a capsule is a set of neurons. Greff et al. and their variants. Many new techniques and architectures are invented, even after the most recently published overview paper on DL. Piotr Mirowski, Yann LeCun, Deepak Madhavan, and Ruben Kuzniecky. (2015) proposed Neural Programmer, an augmented neural network with arithmetic and logic functions. Donahue et al. Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep (2017) proposed Fader Networks, a new type of encoder-decoder architecture to generate realistic variations of input images by changing attribute values. (2016) proposed batch-normalized LSTM (BN-LSTM), which uses batch- normalizing on hidden states of recurrent neural networks. By reviewing a large body of recent related work in literature, … Every now and then, AI bots created with DNN and DRL, are beating human world champions and grandmasters in strategical and other games, from only hours of train- ing. Bradbury et al. VGG Nets use very small convolution filters and depth to 16–19 weight layers. Convolutional layers detect local conjunctions from features and pooling layers merge similar features into one (LeCun et al., 2015). (2015) proposed Dynamic Memory Networks (DMN) for QA tasks. Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. In this study, we survey recent advances in deep learning‐based side‐channel analysis. (2015) proposed Faster Region-based Convolutional Neural Networks (Faster R- CNN), which uses Region Proposal Network (RPN) for real-time object detection. Hinton and Salakhutdinov (2011) proposed a Deep Generative Model using Restricted Boltzmann Machines (RBM) for document processing. Chung et al. function. This paper is an overview of most recent techniques of deep learning, mainly recommended for upcoming researchers in this field. Efficient estimation of word representations in vector space. Deep residual learning for image recognition. It drops units from the neural network along with connections randomly during training. (2017). 12/22/2015 ∙ by Jiuxiang Gu, et al. It is also one of the most popular scientific research trends now-a-days. Zeiler and Fergus (2013) proposed a method for visualizing the activities within CNN. Kurach et al. In this section, we will provide short overview on some major techniques for regularization and optimization of Deep Neural Networks (DNN). (2015)), photographic style transfer (Luan et al., 2017), natural image manifold (Zhu et al., 2016), image colorization (Zhang et al., 2016b)=, image question answering (Yang et al., 2015), generating textures and stylized images (Ulyanov et al., 2016), visualandtextualquestionanswering(Xiongetal. Targ et al. (2015)), named entity recognition (Lample et al., 2016), conversational agents (Ghazvininejad et al., 2017), calling genetic variants (Poplin et al., 2016), X-ray CT reconstruction (Kang et al., 2016). Max-pooling layers down- sample images and keep the maximum value of a sub-region. (2016) presented several methods for training GANs. Capsule network performance on complex data. Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf. In this paper, we presented a discussion about the state-of-the-art approaches as well as the main challenges and opportunities related to this problem. category independent region proposals which defines the set of candidate regions, large Convolutional Neural Network (CNN) for extracting fea- tures from the regions, and a set of class specific linear Support Vector Machines (SVM) (Girshick et al., 2014). Since the beginning of Deep Learning (DL), DL methods are being used in various fields in forms of supervised, unsupervised, semi-supervised or reinforcement learning. Recent advances in computer vision have made accurate, fast and robust Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and ∙ provided detailed overview on the evolution and history of Deep Neural Networks (DNN) as well as Deep Learning (DL). Our paper is mainly for the new learners and novice researchers who are new to this field. Generating neural networks with neural networks. Lample et al. (2015) proposed Highway Networks, which uses gating units to learn reg- ulating information through. and their variants. Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, and • Applicability of RL to multi-stage decision problems in industries is discussed. (2017) proposed Multi-Expert Region-based Convolutional Neural Networks (ME R-CNN), which exploits Fast R-CNN (Girshick, 2015) architecture. When input data is not labeled, unsupervised learning approach is applied to extract features from data and classify or label them. Joseph Redmon, Santosh Kumar Divvala, Ross B. Girshick, and Ali Farhadi. 08/07/2019 ∙ by Yash Mehta, et al. Share. cudnn: Efficient primitives for deep learning. (2015) proposed Deep Residual Learning framework for Deep Neural Networks (DNN), which are called ResNets with lower training error (He, ). (2010) proposed Bidirection LSTM (BLSTM) Recurrent Networks to be used with Dynamic Bayesian Network (DBN) for context-sensitive keyword detection. Deep Learning i.e. Deep Auto-Encoders (DAE) can be transformation-variant, i.e., the extracted features from multilayers of non-linear processing could be changed due to learner. scaling algorithms for larger models and data, reducing optimization difficulties, designing efficient scaling methods etc. An overview of an particular field from couple years back, may turn out to be obsolete today. (2014) proposed Memory Networks for question answering (QA). Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. On deep generative models with applications to recognition. Ranzato et al. share, Brain-Computer Interface (BCI) bridges the human's neural world and the ... http://dl.acm.org/citation.cfm?id=1756006.1756030, http://www.scholarpedia.org/article/Deep_Learning. They showed DL applications in various NLP fields, compared DL models, and discussed possible future trends. http://dl.acm.org/citation.cfm?id=2999134.2999257. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Deep learning is a class of machine learning algorithms that (pp199–200) uses multiple layers to progressively extract higher-level features from the raw input. Razvan Pascanu, Guillaume Desjardins, Joseph P. Turian, David Warde-Farley, It uses lay- ers of capsules instead of layers of neurons, where a capsule is a set of neurons. Weston et al. neuroscience, A Survey of Deep Learning for Scientific Discovery. Xie et al. Deep Learning is one of the newest trends in Machine Learning and Artificial Learning to discover cross-domain relations with generative In recent years, the world has seen many major breakthroughs in this field. Maaløe et al. http://dl.acm.org/citation.cfm?id=3045118.3045338. Supervised learning are applied when data is labeled and the classifier is used for class or numeric prediction. ResNets are considered an important advance in the field of Deep Learning. understanding. Shan Carter, David Ha, Ian Johnson, and Chris Olah. Tran, Bryan Catanzaro, and Evan Shelhamer. Graves et al. As for limitations, the list is quite long as well. This is mostly used for games and robots, solves usually decision making problems (Li, 2017). Deep reinforcement learning: An overview. (2016) provided details of Recurrent and Recursive Neural Networks and architectures, its variants along with related gated and memory networks. Deep neural networks are easily fooled: High confidence predictions Iandola et al. In a deep AE, lower hidden layers are used for encoding and higher ones for decoding, and error back-propagation is used for training (Deng and Yu, 2014). (2015) proposed Faster Region-based Convolutional Neural Networks (Faster R-CNN), which uses Region Proposal Network (RPN) for real-time object detection. Zhizhong Su, Dalong Du, Chang Huang, and Philip H. S. Torr. 28 Zhang et al. discuss about recent advances in Deep Learning for past few years. statistical machine translation. (2017) presented overview on state-of-the-art of DL for remote sensing. (2015) proposed Neural Random Access Machine, which uses an external variable-size random-access memory. (2013b)), generating image captions (Vinyals et al. (2012) proposed Deep Lambertian Networks (DLN) which is a multilayer generative model where latent variables are albedo, surface normals, and the light source. They claimed this architecture is the first VDCNN to be used in text processing which works at the character level. Bahrampour et al. (2017) discussed state-of-the-art deep learning techniques for front-end and back-end speech recognition systems. New York University (NYU), NY, USA. (2015)), document processing (Hinton and Salakhutdinov, 2011), character motion synthesis and editing (Holden et al., 2016), singing synthesis (Blaauw and Bonada, 2017), face recognition and verification (Taigman et al., 2014), action recognition in videos (Simonyan and Zisserman, 2014a), human action recognition (Ji et al., 2013), classifying and visualizing motion capture sequences (Cho and Chen, 2013), handwriting generation and prediction (Carter et al., 2016), automated and machine translation (Wu et al. Bengio (2009) explained deep architectures e.g. An improvement of CapsNet is proposed with EM routing (Anonymous, 2018b). Kaiser and Sutskever (2015) proposed Neural GPU, which solves the parallel problem of NTM (Graves et al., 2014). He et al. (2016) presented several methods for training GANs. Research frontier: Deep machine learning–a new frontier in Two-stream convolutional networks for action recognition in videos. Deep learning techniques currently achieve state of the art performance in a multitude of problem domains (vision, audio, robotics, natural language processing, to name a few). Effective approaches to attention-based neural machine translation. Yangyang Shi, Kaisheng Yao, Le Tian, and Daxin Jiang. We plan to take a broad perspective on RL as a problem setting and cover a wide range of methods: model-free RL, model-based RL, imitation learning, search and trajectory optimization. Deep Auto-Encoders (DAE) can be transformation-variant, i.e., the extracted features from multilayers of non-linear processing could be changed due to learner. Large-scale video classification with convolutional neural networks. https://doi.org/10.1109/IJCNN.2013.6706920. (2015) published a overview of Deep Learning (DL) models with Convo- lutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). proposed Generative Adversarial Nets (GAN) for estimating generative models with an adversarial process. (2016)). (2014), Razavian et al. Augmented Neural Networks are usually made of using extra properties like logic functions along with standard Neural Network architecture. WaveNet is composed of a stack of convolutional layers, and softmax distribution layer for outputs (van den Oord et al., 2016a). (2016), Cho et al. 76 5.2.1 Deep Max-Pooling Convolutional Neural Networks. m... Here, we are going to brief some outstanding overview papers on deep learning. Convolutional layers take input images and generate maps, then apply non-linear activation function. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. Recurrent Neural Networks (RNN) are better suited for sequential inputs like speech and text and generating sequence. Although Deep Learning (DL) has advanced the world faster than ever, there are still ways to go. (2013) proposed Network In Network (NIN). (2016) proposed Layer Normalization, for speeding-up training of deep neural networks especially for RNNs and solves the limitations of batch normalization (Ioffe and Szegedy, 2015). Recent Advances of Deep Learning in Bioinformatics and Computational Biology. Schmidhuber (2014) covered history and evolution of neural networks based on time progression, categorized with machine learning approaches, and uses of deep learning in the neural networks. Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Goodfellow et al. Restricted and Unrestricted Boltzmann Machines and their variants, Deep Boltzmann Machines, Deep Belief Networks (DBN), Directed Generative Nets, and Generative Stochastic Networks etc. For example, people are still dying from hunger and food crisis, cancer and other lethal diseases etc. They described DL from the perspective of Representation Learning, showing how DL techniques work and getting used successfully in various applications, and predicting future learning based on Unsupervised Learning (UL). In this section, we will discuss the main recent Deep Learning (DL) approaches derived from Machine Learning and brief evolution of Artificial Neural Networks (ANN), which is the most common form used for deep learning. (2015) did a comparative study of several deep learning frameworks. (2016) proposed another VDCNN architecture for text classification which uses small convolutions and pooling. (2016) explored RNN models and limitations for language modelling. (2016) proposed a small CNN architecture called SqueezeNet. Neural Turing Machines (NTM), Attentional Interfaces, Neural Pro- grammer and Adaptive Computation Time. Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. Classifying and visualizing motion capture sequences using deep (2017) proposed a CNN architecture for sequence-to-sequence learning. Salimans et al. ANNs with many hidden layers (Bengio, 2009), . (2015) proposed a CNN architecture named YOLO (You Only Look Once) for unified and real-time object detection. batch-normalized models. (2012), Zhang et al. Bougares, Holger Schwenk, and Yoshua Bengio. Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. Deep architectures are multilayer non-linear repetition of simple architectures in most of the cases, which helps to obtain highly complex functions out of the inputs (LeCun et al., 2015). Also we hope to pay some tributes by this work, to the top DL and ANN researchers of this era, Geoffrey Hinton (Hinton, ), Juergen Schmidhuber (Schmidhuber, ), Yann LeCun (LeCun, ), Yoshua Bengio (Bengio, ) and many others who worked meticulously to shape the modern Artificial Intelligence (AI). Highway long short-term memory rnns for distant speech recognition. Salakhutdinov. Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho (2017) proposed Mask Region-based Convolutional Network (Mask R-CNN) in- stance object segmentation. Overview papers are found to be very beneficial, especially for new researchers in a particular field. (2015) proposed Deep Residual Learning framework for Deep Neural Networks (DNN), which are called ResNets with lower training error (He). Shabanian et al. James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, (2014) showed that Deep Neural Networks (DNN) can be easily fooled while recognizing images. Deutsch (2018) used Hyper Networks for generating neural networks. Deng and Yu (2014) provided detailed lists of DL applications in various categories e.g. For that purpose, we will try to give a basic and clear idea of deep learning to the new researchers and anyone interested in this field. They claimed that eight variants of LSTM failed to perform significant improvement, while only Vanilla LSTM performs well (Greff et al., 2015). human and machine translation. It augments convolutional residual networks with a long short term memory mechanism (Moniz and Pal, 2016). (2015)), video classification (Karpathy et al., 2014), defect classification (Masci et al., 2013b), text, speech, image and video processing (LeCun et al., 2015), text classification (Conneau et al., 2016), speech recognition and spoken language understanding (Hinton et al. Neural networks work with functionalities similar to human brain. As for limitations, the list is quite long as well. (2015)), document processing (Hinton and Salakhutdinov, 2011), character motion synthesis and editing (Holden et al., 2016), singing synthesis (Blaauw and Bonada, 2017), face recognition and verification (Taigman et al., 2014), action recognition in videos (Simonyan and Shikhar Sharma, Ryan Kiros, and Ruslan Salakhutdinov. van den Oord et al. ME R-CNN generates Region of Interests (RoI) from selective and exhaustive search. What’s next When first published in August 2018, the CoQA baseline automated system had an F1 score of 65.4%, well below the human performance of 88.8%. We offer a taxonomical study of text representations, learning model, evaluation, metrics, and implications of recent advances in deep learning architectures. Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alexander J. Smola. They claimed this architecture is the first VDCNN to be used in text processing which works at the character level. Here’s how deep learning evolved in 2020. learning algorithm. Recent advances in Deep Learning also incorporate ideas from statistical learning [1,2], reinforcement learning (RL) [3], and numerical optimization. ResNets have lower error and easily trained with Residual Learning. (2015) proposed Highway Networks, which uses gating units to learn regulating information through. LSTMs. Marcus (2018) gave an important review on Deep Learning (DL), what it does, its limits and its nature. For Artificial Neural Networks (ANN), Deep Learning (DL) aka hierarchical learning (Deng and Yu, 2014) is about assigning credits in many computational stages accurately, to transform the aggregate activation of the network (Schmidhuber, 2014). Visualizing and understanding convolutional networks. Fabio Augusto González Osorio. overview of recent developments. learning. Variational Bi-LSTM creates a channel of information exchange be- tween LSTMs using Variational Auto-Encoders (VAE), for learning better representations (Shabanian et al., 2017). Denton et al. learning techniques are being born, outperforming state-of-the-art machine Deep learning in remote sensing: a review. Get the latest machine learning methods with code. In this video from Switzerland HPC Conference, Zaikun Xu from DeepCube presents: Recent Advances in Deep Learning. (2016) proposed Zoneout, a regularization method for Recurrent Neural Networks (RNN). share. Emily L. Denton, Soumith Chintala, Arthur Szlam, and Robert Fergus. (2014) proposed Long-term Recurrent Convolutional Networks (LRCN), which uses CNN for inputs, then LSTM for recurrent sequence modeling and generating predictions. processing. Augmented Neural Networks are usually made of using extra properties like logic functions along with standard Neural Network architec- ture (Olah and Carter, 2016). ∙ He et al. Bengio (2009) explained deep architectures e.g. Four basic ideas make the Convolutional Neural Networks (CNN), i.e., local connections, shared weights, pooling, and using many layers. Fractals are repeated architecture generated by simple expansion rule (Larsson et al., 2016). Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, He focused on many challenges of Deep Learning e.g. 07/21/2018 ∙ by Matiur Rahman Minar, et al. Bansal et al. (2015) proposed Conditional Random Fields as Recurrent Neural Networks (CRF-RNN), which combines the Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for probabilistic graphical modelling. Ba et al. speech recognition, handwriting recognition, and polyphonic music modeling. neural networks and generative models for AI. Schmidhuber (2014) described neural networks for unsu- pervised learning as well. (2016b) proposed Pixel Recurrent Neural Networks (PixelRNN), made of up to twelve two-dimensional LSTM layers. (2016), (DBLP:journals/corr/AntolALMBZP15)), visual recognition and description (Donahue et al. He also mentioned that DL assumes stable world, works as approximation, is difficult to engineer and has potential risks as being an excessive hype. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore (2016) proposed Zoneout, a regularization method for Recurrent Neural Networks (RNN). Lei Yin, Zhi Zhang, Yingze Liu, Yin Gao, Jingkai Gu, Recent advances in single-cell analysis by mass spectrometry, The Analyst, 10.1039/C8AN01190G, (2018). Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, (2017a) etc. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Denton et al. (2016), Wang et al. Discovering binary codes for documents by learning deep generative Schmidhuber (2014) mentioned full history of neural networks from early neural networks to recent successful techniques. Fast image scanning with deep max-pooling convolutional neural Deep Metric Learning for Visual Understanding: An Overview of Recent Advances @article{Lu2017DeepML, title={Deep Metric Learning for Visual Understanding: An Overview of Recent Advances}, author={Jiwen Lu and J. Hu and J. Zhou}, journal={IEEE Signal Processing Magazine}, year={2017}, volume={34}, pages={76-84} } Blocks and fuel: Frameworks for deep learning. When input data is not labeled, unsupervised learning approach is applied to extract fea- tures from data and classify or label them. Keywords: Neural Networks, Machine Learning, Deep Learning, Recent Advances, Overview. Kaiser and Sutskever (2015) proposed Neural GPU, which solves the parallel problem of NTM (Graves et al., 2014). Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Maaløe et al. RPN is a fully convolutional network which generates region proposals accurately and efficiently (Ren et al., 2015). for keyword spotting. Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton. (2016b) proposed Deep Long Short-Term Memory (DLSTM), which is a stack of LSTM units for feature mapping to learn representations (Shi et al., 2016b). In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Though Deep Learning has achieved tremendous success in many areas, it still has long way to go. Goodfellow et al. Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Autoencoders (AE) are neural networks (NN) where outputs are the inputs. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. (2010) provided a short overview on recent DL techniques. DMN has four modules i.e. (2015), van Hasselt et al. Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah. Angel Alfonso Cruz-Roa, John Edison Arevalo Ovalle, Anant Madabhushi, and International Conference on Recent Advances in Deep Learning Technologies. Representation learning: A review and new perspectives. Other techniques and neural networks came as well e.g. Abstract: Deep learning is becoming a mainstream technology for speech recognition at industrial scale. Such as Theano. MPCNN generally consists of three types of layers other than the input layer. Join one of the world's largest A.I. Dynamic memory networks for visual and textual question answering. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. Input, Question, Episodic Memory, Output (Kumar et al., 2015). Zoneout uses noise randomly while training similar to Dropout (Srivastava et al., 2014), but preserves hidden units instead of dropping (Krueger et al., 2016). description. convolutional networks. Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. (2017), Ranzato et al. Milon Islam, et al. Peng and Yao (2015) proposed Recurrent Neural Networks with External Memory (RNN- EM) to improve memory capacity of RNNs. Recent Advances in Deep Learning: An Overview. verification. Google’s neural machine translation system: Bridging the gap between Overview papers are found to be very beneficial, especially for new researchers in a particular field. However, there are many difficult problems for humanity to deal with. Attention and augmented recurrent neural networks. (2016) developed a class for one-shot generalization of deep generative models. In particular, the recent introduction of deep learning to supervised speech separation has dramatically accelerated progress and boosted separation performance. He et al. Highway long short-term memory RNNS for distant speech recognition. He also discussed deep neural networks and deep learning to some extent. Caffe: Convolutional architecture for fast feature embedding. Simonyan and Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VD- CNN) architecture, also known as VGG Nets. (2012), Zhang et al. convolutional neural networks. (2015) proposed a Deep Generative Model (DGM) called Laplacian Generative Adversarial Networks (LAPGAN) using Generative Adversarial Networks (GAN) approach. Deep Metric Learning for Visual Understanding: An Overview of Recent Advances Abstract: In this article, we have summarized the recent trends of DML and shown their wide applications of various visual understanding tasks including face recognition, image classification, visual search, person reidentification, visual tracking, cross-modal matching, and image set classification. speech and audio processing, information retrieval, object recognition and computer vision, multimodal and multi-task learning etc. Deep Belief Networks (DBN) are generative models with several layers of latent binary or real variables (Goodfellow et al., 2016). Glass. A theory of deep learning that explains why and how deep networks work, and what their limitations are, will likely allow development of even much more powerful learning … Reinforcement learning uses reward and punishment system for the next move generated by the learning model. Shubho Sengupta, Yi Wang, Zhiqian Wang, Chong Wang, Bo Xiao, Dani Yogatama, CapsNet usually contains several convolution layers and on capsule layer at the end (Xi et al., 2017). (2014)), object detection (Lee et al. (2014) showed that Deep Neural Networks (DNN) can be easily fooled while recognizing images. An (incomplete) overview of recent advances on the topic of Deep Learning Landscape. Wang et al. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, They claimed to train ultra deep neural networks without residual learning. Also it uses per-RoI multi-expert network instead of single per-RoI network. Start- ing from Machine Learning (ML) basics, pros and cons for deep architectures, they con- cluded recent DL researches and applications thoroughly. Deep Neural Networks (DNN) gained huge success in Supervised Learning (SL). Deep learning for environmentally robust speech recognition: An Ross B. Girshick, Sergio Guadarrama, and Trevor Darrell. Wu et al. We are still away from fully understanding of how deep learning works, how we can get machines more smarter, close to or smarter than humans, or learning exactly like human. (2016) wrote and skillfully explained about Deep Feedforward Networks, Convolutional Networks, Recurrent and Recursive Networks and their improvements. Recent Advances in Deep Learning: An Overview Matiur Rahman Minar minar09.bd@gmail.com Jibon Naher jibon.naher09@gmail.com Department of Computer Science and … Goodfellow et al. James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. (2015) presented a nice overview on recent advances of CNNs, multiple variants of CNN, its architectures, regularization methods and functionality, and applications in various fields. Using Deep Reinforcement Learning (DRL) for mastering games has become a hot topic now-a-days. The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Rethage et al. ∙ University of Wisconsin-Madison ∙ 0 ∙ share . Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Cory Y. McLean, and Mark A. DePristo. Srivastava et al. Now-a-days, scientific research is an attractive profession since knowledge and education are more shared and available than ever. Tea/coffee and light refreshment provided. Arel et al. http://dl.acm.org/citation.cfm?id=3045390.3045543. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Schmidhuber (2014) covered all neural networks starting from early neural networks to recently successful Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and their improvements. Discussion and Conclusion. Bengio (2009) discussed deep architectures i.e. Boltzmann Machines are connectionist approach for learning arbitrary probability distribu- tions which use maximum likelihood principle for learning (Goodfellow et al., 2016). Bengio (2009) explained neural networks for deep architectures e.g. To sum it accurately, Deep Learning is a sub-field of Machine Learning, which uses many levels of non-linear information processing and abstraction, for supervised or unsupervised feature learning and representation, classification and pattern recognition. Karpathy et al. Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Adversarial attacks on neural network policies. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and Deep Learning i.e. for scalable spatiotemporal pattern inference. http://doi.acm.org/10.1145/2897824.2925975. (2017b), Silver et al. Gated feedback recurrent neural networks. Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. (2015) used character-level language models for analyzing and visualizing predictions, representations training dynamics, and error types of RNN and its variants e.g. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Abstract: Deep learning is becoming a mainstream technology for speech recognition at industrial scale. Tensorflow: Large-scale machine learning on heterogeneous distributed Binhua Tang 1,2 * †, Zixiang Pan 1 †, Kang Yin 1 and Asif Khateeb 1. Zisserman (2014b), Krizhevsky et al. Ian Lenz, Honglak Lee, and Ashutosh Saxena. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. In this paper, we are going to briefly Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott E. Reed, Advances in Deep Learning 2020. Srivastava et al. This architecture consists of three modules i.e. For example, Nguyen et al. Schmidhuber (2015) did a generic and historical overview of Deep Learning along with CNN, RNN and Deep Reinforcement Learning (RL). Sabour et al. Neural machine translation by jointly learning to align and In this paper, firstly we will provide short descriptions of the past overview papers on deep learning models and approaches. The term ”Deep Learning” (DL) was first introduced to Machine Learning (ML) in 1986, and later used for Artificial Neural Networks (ANN) in 2000 (Schmidhuber, 2015). Where: Amsterdam, Netherlands. (2016) proposed Layer Normalization, for speeding-up training of deep neural networks especially for RNNs and solves the limitations of batch normalization (Ioffe and Szegedy, 2015). Gao, Wen-tau Yih, and Michel Galley. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. van den Oord et al. Since deep learning is Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. (Click heading for the reference) Parametric Rectifier Linear Unit (PReLU) The idea is to allow negative activation in well-known ReLU units by controlling it with a learnable parameter. Itamar Arel, Derek C. Rose, and Thomas P. Karnowski. (2013b)), generating image captions (Vinyals et al. Denoyer, and Marc’Aurelio Ranzato. (2017), Ranzato et al. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. It is also one of the most popular scientific research Rezende et al. supervised and unsupervised networks, optimization and training models from the perspective of representation learning. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. Tür, Dong Yu, and Geoffrey Zweig. (2014) proposed Dropout to prevent neural networks from overfitting. (2017a) described the evolution of deep learning models in time-series man- ner. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Deep learning is becoming a mainstream technology for speech recognition at industrial scale. (2015)), text-to-speech generation (Wang et al. In recent years, the world For a technological research trend, its only normal to assume that there will be numerous advances and improvements in various ways. (2016) provided details of Recurrent and Recursive Neural Networks and architectures, its variants along with related gated and memory networks. Goodfellow et al. By reviewing a large body of recent related work in literature, we systematically analyze the existing … Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. They also pointed out the articles of major advances in DL in the bibliography. Nonetheless, there are some limitations and important aspects that need to be addressed. van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda ∙ The architecture used Graphics Processing Units (GPU) for convolution operation, Rectified Linear Units (ReLU) as activation function and Dropout (Srivastava et al., 2014) to reduce overfitting. ResNext exploits ResNets (He et al., 2015) for repeating layers with split-transform-merge strategy (Xie et al., 2016). As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning … proposal networks. (2015a) proposed Deep Neural Support Vector Machines (DNSVM), which uses Support Vector Machine (SVM) as the top layer for classification in a Deep Neural Network (DNN), 5.18 Convolutional Residual Memory Networks. Texture networks: Feed-forward synthesis of textures and stylized Asynchronous methods for deep reinforcement learning. Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VDCNN) architecture, also known as VGG Nets. Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Zhang et al. Andrew Ng from Coursera and Chief Scientist at Baidu Research formally founded Google Brain that eventually resulted in the productization of deep learning technologies across a large number of Google services.. Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM) etc. along with optimistic DL researches. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. Mastering chess and shogi by self-play with a general reinforcement Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM) etc. Recent Advances in Deep Learning: An Overview. Larsson et al. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Image from eventbrite.com.au . It is often hard to keep track with contemporary advances in a research area, provided that field has great value in near future and related applications. VAEs are built upon standard neural networks and can be trained with stochastic gradient descent. Zhu et al. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. In this section, we will briefly discuss about the deep neural networks (DNN), and recent improvements and breakthroughs of them. Recent advances in deep learning models for skin cancer detection have been showing the potential of this technique to deal with this task. We are still away from fully understanding of how deep learning works, how we can get machines more smarter, close to or smarter than humans, or learning exactly like human. Karpathy et al. Boltzmann Machines are connectionist approach for learning arbitrary probability distributions which use maximum likelihood principle for learning (Goodfellow et al., 2016). covered all neural networks starting from early neural networks to recently successful Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and their improvements. (2014), Bahdanau et al. These are composed on neurons and connections mainly. (2011) built a deep generative model using Deep Belief Network (DBN) for images recognition. Krueger et al. Hinton and Salakhutdinov (2011) proposed a Deep Generative Model using Restricted Boltzmann Machines (RBM) for document processing. Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Srivastava et al. Although Deep Learning (DL) has advanced the world faster than ever, there are still ways to go. (2015) proposed Residual Networks (ResNets) consists of 152 layers. Zoneout uses noise randomly while training similar to Dropout (Srivastava et al., 2014), but preserves hidden units instead of dropping (Krueger et al., 2016). From that point, ANNs got improved and designed in various ways and for various purposes. He strongly pointed out the limitations of DL methods, i.e., requiring more data, having limited capacity, inability to deal with hierarchical structure, struggling with open-ended inference, not being sufficiently transparent, not being well integrated with prior knowledge, and inability to distinguish causation from correlation (Marcus, 2018). Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. ∙ (2016a) presented an experimental framework for understanding deep learning models. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. Sengupta, and Mohammad Shoeybi. Tip: you can also follow us on Twitter (2017b), Silver et al. Itamar Arel, Derek Rose, and Tom Karnowski. In recent … ResNext exploits ResNets (He et al., 2015) for repeating layers with split-transform-merge strategy (Xie et al., 2016). Four basic ideas make the Convolutional Neural Networks (CNN), i.e., local connections, shared weights, pooling, and using many layers. talked about DL models and architectures, mainly used in Natural Language Processing (NLP). Alex Graves, Greg Wayne, and Ivo Danihelka. (2016), Wang et al. Deng and Yu (2014) described deep learning classes and techniques, and applications of DL in several areas. LeCun et al. (2016a), Mesnil et al. Crossref Volume 120 , Issue 1 Deep speech 2: End-to-end speech recognition in english and mandarin. Deep learning is becoming a mainstream technology for speech recognition at industrial scale. Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Here, we are going to brief some outstanding overview papers on deep learning. Each expert is the same architecture of fully connected layers from Fast R-CNN (Lee et al., 2017). (2015a) proposed Deep Neural Support Vector Machines (DNSVM), which uses Support Vector Machine (SVM) as the top layer for classification in a Deep Neural Network (DNN). Abstract. In Stacked Denoising Auto-Encoders (SDAE), encoding layer is wider than the input layer (Deng and Yu, 2014). Simonyan and Fractals are repeated architecture generated by simple expansion rule (Larsson et al., 2016). In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years. (2016) proposed Quasi Recurrent Neural Networks (QRNN) for neural sequence modelling, appling parallel across timesteps. Second generation used Backpropagation to update weights of neurons according to error rates. Get the latest machine learning methods with code. (2015)), Chess and Shougi (Silver et al., 2017a). However, DL is a highly flourishing field right now. • The idea of RL and its success in the Go game (a la AlphaGo) are introduced. Recent advances in Deep Learning also incorporate ideas from statistical learning [1,2], reinforcement learning (RL) [3], and numerical optimization. Geoffrey Hinton and Ruslan Salakhutdinov. Also, there are two brief sections for open-source DL frameworks and significant DL applications. Deng and Yu (2014) described deep learning classes and techniques, and applications of DL in several areas. The model also uses convolutional networks within a Laplacian pyramid framework (Denton et al., 2015). The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. adversarial networks. Girshick (2015) proposed Fast Region-based Convolutional Network (Fast R-CNN). ME R-CNN: multi-expert region-based CNN for object detection. (2016), Cho et al. neural networks into compressed and smaller model. • Epileptic Seizure Prediction (Mirowski et al., 2008) • hardware acceleration (Han et al., 2016) • robotics (Lenz et al., 2013). 07/09/2018 ∙ by Emilia Gómez, et al. (2016) discussed deep networks and generative models in details. (2017) proposed a WaveNet model for speech denoising. Salimans et al. (2016) proposed HyperNetworks which generates weights for other neural networks, such as static hypernetworks convolutional networks, dynamic hypernetworks for recurrent networks.