Kaiser and Sutskever (2015) proposed Neural GPU, which solves the parallel problem of NTM (Graves et al., 2014). These are used to train an artificial neural network to detect objects with high precision in new examples of images it’s never seen before. Aayush Bansal, Xinlei Chen, Bryan C. Russell, Abhinav Gupta, and Deva Ramanan. Deep Learning Landscape. An updated overview of recent gradient descent algorithms. Ba et al. share, Over the past few years, we have seen fundamental breakthroughs in core Srivastava et al. This article includes the basic idea of DL, major approaches and methods, recent breakthroughs and applications. (2015) proposed Gated Feedback Recurrent Neural Networks (GF-RNN), which extends the standard RNN by stacking multiple recurrent layers with global gating units. This paper would be a good read to know the origin of the Deep Learning in evolutionary manner. Other techniques and neural networks came as well e.g. (2017), Ranzato et al. Deng and Yu (2014) described deep learning classes and techniques, and applications of DL in several areas. Marcus (2018) thinks DL needs to be reconceptualized and to look for possibilities in unsupervised learning, symbol manipulation and hybrid models, having insights from cognitive science and psychology and taking bolder challenges. RNNs used to be difficult to train because of gradient vanishing and exploding problem (LeCun et al., 2015). Representation learning: A review and new perspectives. Learning and transferring mid-level image representations using Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Mastering chess and shogi by self-play with a general reinforcement (2015), Luong et al. Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VDCNN) architecture, also known as VGG Nets. (2015) published a overview of Deep Learning (DL) models with Convo- lutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). (2017) proposed Capsule Networks (CapsNet), an architecture with two convolutional layers and one fully connected layer. Generating sequences with recurrent neural networks. They also discussed open-source DL frameworks and other technical details for deep learning. The network composed of five convolutional layers and three fully connected layers. 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. Tür, Dong Yu, and Geoffrey Zweig. Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. Experiments in handwriting with a neural network. http://dx.doi.org/10.1109/MCI.2010.938364. adversarial networks. Deep Learning is one of the newest trends in Machine Learning and Artificial Recent Advances in Deep Learning: An Overview. When input data is not labeled, unsupervised learning approach is applied to extract features from data and classify or label them. (2016), Kim et al. 05/08/2020 ∙ by Siddhant Garg, et al. 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). Ranzato et al. About: International Conference on Recent Advances in Deep Learning Technologies is another conference that is organised by The International Research Conference. (2016) proposed Quasi Recurrent Neural Networks (QRNN) for neural sequence modelling, appling parallel across timesteps. They claimed this architecture is the first VDCNN to be used in text processing which works at the character level. (2016) discussed deep networks and generative models in details. (2017) proposed a CNN architecture for sequence-to-sequence learning. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan (2015) proposed Highway Networks, which uses gating units to learn regulating information through. 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. (2015)), photographic style transfer (Luan et al., 2017), natural image manifold (Zhu et al., 2016), image question answering (Yang et al., 2015), generating textures and stylized images (Ulyanov et al., 2016), visual and textual question answering (Xiong et al. RHNs use Highway layers inside the recurrent transition (Zilly et al., 2017). Shikhar Sharma, Ryan Kiros, and Ruslan Salakhutdinov. Soheil Bahrampour, Naveen Ramakrishnan, Lukas Schott, and Mohak Shah. https://doi.org/10.1109/TNNLS.2016.2582924. Marcus (2018) gave an important review on Deep Learning (DL), what it does, its limits and its nature. Dilek Z. Hakkani-Tür, Xiaodong He, Larry P. Heck, Gökhan (2017) proposed Multi-Expert Region-based Convolutional Neural Networks (ME R-CNN), which exploits Fast R-CNN (Girshick, 2015) architecture. Recent advances in Deep Learning also incorporate ideas from statistical learning [1,2], reinforcement learning (RL) [3], and numerical optimization. Zhang et al. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Bengio et al. Blocks and fuel: Frameworks for deep learning. translate. Batch normalization: Accelerating deep network training by reducing Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. 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. Srivastava et al. Recent advances in deep learning and transfer learning have resulted in breakthrough leaps in what’s newly achievable in natural language understanding (NLU). Donahue et al. along with optimistic DL researches. proposed batch-normalized LSTM (BN-LSTM), which uses batch-normalizing on hidden states of recurrent neural networks. Arel et al. Deep Stacking Network (DSN) and its variants. Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM) etc. All recent overview papers on Deep Learning (DL) discussed important things from several perspectives. 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). Piotr Mirowski, Yann LeCun, Deepak Madhavan, and Ruben Kuzniecky. Two-stream convolutional networks for action recognition in videos. (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. (2017) proposed Mask Region-based Convolutional Network (Mask R-CNN) in- stance object segmentation. DMN has four modules i.e. They explored various methods and models from the perspectives of applications, techniques and challenges. Shan Carter, David Ha, Ian Johnson, and Chris Olah. Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Goodfellow et al. Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Max-Pooling Convolutional Neural Networks (MPCNN) operate on mainly convolutions and max-pooling, especially used in digital image processing. (2015b), Zhang et al. Goodfellow et al. Zilly et al. Maxime Oquab, Leon Bottou, Ivan Laptev, and Josef Sivic. (2017a) etc. Here, we are going to brief some outstanding overview papers on deep learning. FractalNet, as an alternative to residual nets. This is mostly used for games and robots, solves usually decision making problems (Li, 2017). Gu et al. (2017) etc. They claimed to train ultra deep neural networks without residual learning. Huang et al. Dropout: A simple way to prevent neural networks from overfitting. (2015) proposed Highway Networks, which uses gating units to learn reg- ulating information through. (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). Deep reinforcement learning with double q-learning. 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. (2017) proposed Fader Networks, a new type of encoder-decoder architecture to generate realistic variations of input images by changing attribute values. and their variants. Max-Pooling Convolutional Neural Networks (MPCNN) operate on mainly convolutions and max-pooling, especially used in digital image processing. Hyungtae Lee, Sungmin Eum, and Heesung Kwon. Pierce, Peter Ondruska, Ishaan Gulrajani, and Richard Socher. ... Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Karpathy et al. DLN is a combination of lambertian reflectance with Gaussian Restricted Boltzmann Machines and Deep Belief Networks (Tang et al., 2012). RNNs used to be difficult to train because of gradient vanishing and exploding problem (LeCun et al., 2015). Jonathan Masci, Ueli Meier, Gabriel Fricout, and Jürgen Schmidhuber. compositionality. a discriminative model to learn model or data dis- tribution (Goodfellow et al., 2014). neural networks and generative models for AI. Using Deep Reinforcement Learning (DRL) for mastering games has become a hot topic now-a-days. Richard Zhang, Phillip Isola, and Alexei A. Efros. Chung et al. Bidirectional lstm networks for context-sensitive keyword detection A very recent proposed improvement of dropout is Fraternal Dropout (Anonymous, 2018a) for Recurrent Neural Networks (RNN). Learning from limited data and generalization will become central theme’s of RL research; Breakthroughs in this domain will be closely tied to advances in the Deep Learning field in general, as the shortcomings they address are fundamental to neural networks as function approximators rather than to the Reinforcement Learning paradigm. They showed DL applications in various NLP fields, compared DL models, and discussed possible future trends. Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. Dropout is a neural network model-averaging regularization method by adding noise to its hidden units. Wei. 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). MILA, University of Montreal, Quebec, Canada. He emphasized on sequence-processing RNNs, while pointing out the limitations of fundamental DL and NNs, and the tricks to improve them. http://dx.doi.org/10.1111/j.1756-8765.2010.01109.x. Deep generative image models using a laplacian pyramid of adversarial (2016),?DBLP:journals/corr/AntolALMBZP15)), visual recognition and description (Donahue et al. Recent Advances of Deep Learning in Bioinformatics and Computational Biology. Ting Liu, Xingxing Wang, and Gang Wang. Bengio (2009) explained neural networks for deep architectures e.g. Finally, we will discuss about current status and the future of Deep Learning in the last two sections i.e. (2017) proposed a WaveNet model for speech denoising. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. GAN architecture is composed of a generative model pitted against an adversary i.e. understanding. 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. internal covariate shift. Supervised learning are applied when data is labeled and the classifier is used for class or numeric prediction. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John (2016) developed a class for one-shot generalization of deep generative models. Schmidhuber (2014) described advances of deep learning in Reinforce- ment Learning (RL) and uses of Deep Feedforward Neural Netowrk (FNN) and Recurrent Neural Network (RNN) for RL. Conneau et al. Deep learning of representations: Looking forward. Wang et al. ∙ verification. ∙ (2016), Cho et al. share, Recent advances in computer vision have made accurate, fast and robust Multi-class generative adversarial networks with the L2 loss (2017) talked about DL models and architectures, mainly used in Natural Language Processing (NLP). (2017)), sentence modelling (Kalchbrenner et al., 2014), document and sentence processing (Le and Mikolov (2014), Mikolov et al. For example, people are still dying from hunger and food crisis, cancer and other lethal diseases etc. (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. A deep learning architecture comprising homogeneous cortical circuits Also, previous papers focus from different perspectives. Conditional random fields as recurrent neural networks. Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. http://dl.acm.org/citation.cfm?id=1756006.1756030, http://www.scholarpedia.org/article/Deep_Learning. A convolutional neural network for modelling sentences. Convolutional layers detect local conjunctions from features and pooling layers merge similar features into one (LeCun et al., 2015). When input data is not labeled, unsupervised learning approach is applied to extract fea- tures from data and classify or label them. (2015) proposed Deep Residual Learning framework for Deep Neural Networks (DNN), which are called ResNets with lower training error (He). Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Restricted Boltzmann Machines (RBM) are special type of Markov random field containing one layer of stochastic hidden units i.e. Abstract: Deep learning is becoming a mainstream technology for speech recognition at industrial scale. (2017) proposed Variational Bi-LSTMs, which is a variant of Bidirectional LSTM architecture. understanding. CapsNet usually contains several convolution layers and on capsule layer at the end (Xi et al., 2017). Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. • The idea of RL and its success in the Go game (a la AlphaGo) are introduced. (2016) proposed Resnet in Resnet (RiR) which combines ResNets (He et al., 2015) and standard Convolutional Neural Networks (CNN) in a deep dual stream architecture (Targ et al., 2016). You only look once: Unified, real-time object detection. neural networks into compressed and smaller model. 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%. (2013) discussed on Representation and Feature Learning aka Deep Learn- ing. Neural programmer: Inducing latent programs with gradient descent. 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.. segmentation. Nielsen (2015) described the neural networks in details along with codes and examples. An improvement of CapsNet is proposed with EM routing (Anonymous, 2018b). LSTMs. IDSIA, USI. Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. Rigoll. Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Deep speech 2: End-to-end speech recognition in english and mandarin. DMN has four modules i.e. 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. Chris Dyer. speech and audio processing, information retrieval, object recognition and computer vision, multimodal and multi-task learning etc. For a technological research trend, its only normal to assume that there will be numerous advances and improvements in various ways. Wed 1 May 2019 Wednesday 1 May 2019 5:30 PM - 11:59 PM . Get the latest machine learning methods with code. 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). Memory Networks are composed of memory, input feature map, generalization, output feature map and response (Weston et al., 2014) . (2012), He et al. By reviewing a large body of recent related work in literature, we systematically analyze the existing … Comparative study of caffe, neon, theano, and torch for deep (2015) proposed Deep Residual Learning framework for Deep Neural Networks (DNN), which are called ResNets with lower training error (He, ). For example, AlphaGo and AlphaGo Zero for game of GO (Silver et al. (2015)), Chess and Shougi (Silver et al., 2017a). Transform- ing Auto-Encoders (TAE) work with both input vector and target output vector to apply transformation-invariant property and lead the codes towards a desired way (Deng and Yu, 2014). Boltzmann Machines are connectionist approach for learning arbitrary probability distributions which use maximum likelihood principle for learning (Goodfellow et al., 2016). (2015) provided a brief yet very good explanation of supervised learning approach and how deep architectures are formed. They explored various methods and models from the perspectives of applications, techniques and challenges. Deep Belief Networks (DBN) are generative models with several layers of latent binary or real variables (Goodfellow et al., 2016). Hinton et al. (2010) proposed Bidirection LSTM (BLSTM) Recurrent Networks to be used with Dynamic Bayesian Network (DBN) for context-sensitive keyword detection. Emily L. Denton, Soumith Chintala, Arthur Szlam, and Robert Fergus. (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. Convolutional layers detect local conjunctions from features and pooling layers merge similar features into one (LeCun et al., 2015). (2017) presented overview on state-of-the-art of DL for remote sensing. (2016) proposed Fractal Networks i.e. Simonyan and Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Classifying and visualizing motion capture sequences using deep Young et al. 0 For example, people are still dying from hunger and food crisis, cancer and other lethal diseases etc. 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.. Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Fethi (2017) provided large-scale analysis of Vanilla LSTM and eight LSTM vari- ants for three uses i.e. Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, Erich AE takes the original input, encodes for compressed representation and then decodes to reconstruct the input (Wang, ). CNN features off-the-shelf: an astounding baseline for recognition. (2017) proposed PixelNet, using pixels for representations. Zilly et al. There were many overview papers on Deep Learning (DL) in the past years. We would like to thank Dr. Mohammed Moshiul Hoque, Professor, Department of CSE, CUET, for introducing us to the amazing world of Deep Learning. Deng and Yu (2014) provided detailed lists of DL applications in various categories e.g. Impact on Singers and Listeners, Recent Trends in Deep Learning Based Personality Detection, A Survey on Deep Learning based Brain Computer Interface: Recent One-shot generalization in deep generative models. ∙ Wavenet: A generative model for raw audio. Peng and Yao (2015) proposed Recurrent Neural Networks with External Memory (RNN-EM) to improve memory capacity of RNNs. (2017) proposed an architecture for adersarial attacks on neural networks, where they think future works are needed for defenses against those attacks. 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). ∙ Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, (2016) proposed HyperNetworks which generates weights for other neural networks, such as static hypernetworks convolutional networks, dynamic hypernetworks for recurrent networks. In recent years, the world Advances in Deep Learning 2020. Keywords: Neural Networks, Machine Learning, Deep Learning, Recent Advances, Overview. Jürgen Schmidhuber. Although Deep Learning (DL) has advanced the world faster than ever, there are still ways to go. Mnih et al. Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Tea/coffee and light refreshment provided. He also mentioned that DL assumes stable world, works as approximation, is difficult to engineer and has potential risks as being an excessive hype. (2016c), Zhang et al. NTMs usually combine RNNs with external memory bank (Olah and Carter, 2016). Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo and Yoshua Bengio. In this lecture, I will cover some of recent advances (made mostly in the last 5 years) in this area. Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor S. Lempitsky. Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. ∙ 1 ∙ share . Goodfellow et al. As for limitations, the list is quite long as well. (2017) discussed state-of-the-art deep learning techniques for front-end and back-end speech recognition systems. Sukthankar, and Li Fei-Fei. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.. Overview. (2016) proposed Quasi Recurrent Neural Networks (QRNN) for neural sequence modelling, appling parallel across timesteps. AE and its variants. Max-pooling layers down- sample images and keep the maximum value of a sub-region. Our paper is mainly for the new learners and novice researchers who are new to this field. Four basic ideas make the Convolutional Neural Networks (CNN), i.e., local connections, shared weights, pooling, and using many layers. Feedforward Neural Networks (FNN), Convolutional Neural Netowrks (CNN), Recurrent Neural Networks (RNN) etc. Fast image scanning with deep max-pooling convolutional neural 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. Simonyan and Zisserman (2014b) proposed Very Deep Convolutional Neural Network (VD- CNN) architecture, also known as VGG Nets. (2016a), Mesnil et al. 8 and Josef Urban. Li (2017) discussed Deep Reinforcement Learning(DRL), its architectures e.g. Geoffrey Hinton and Ruslan Salakhutdinov. Advances and New Frontiers, A Review on Deep Learning Techniques for the Diagnosis of Novel (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), Epileptic Seizure Prediction (Mirowski et al., 2008). (2017), Ranzato et al. Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan This course aims to provide an overview of the recent developments in RL combined with advances in deep learning. (2014) proposed Region-based Convolutional Neural Network (R-CNN) which uses regions for recognition. Boltzmann Machines (BM) and Restricted Boltzmann Machines (RBM) etc. Ha et al. Memory Networks are composed of memory, input feature map, generalization, output feature map and response (Weston et al., 2014) . Ozair, Ryan Prenger, Jonathan Raiman, Sanjeev Satheesh, David Seetapun, http://dx.doi.org/10.1109/CVPR.2011.5995710. Its also important to follow their works to stay updated with state-of-the-art in DL and ML research. Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton. • Arel et al. Using Deep Reinforcement Learning (DRL) for mastering games has become a hot topic now-a-days. (2016) explored RNN models and limitations for language modelling. (2016c), Zhang et al. Mastering the game of go with deep neural networks and tree search. Learning phrase representations using RNN encoder-decoder for Graves et al. Karpathy et al. (2017b), Arik et al. van den Oord 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). They described DL methods and approaches in great ways as well as their applications and directions for future research. Show and tell: A neural image caption generator. neural networks. He et al. Bansal et al. (2014), Razavian et al. We offer a taxonomical study of text representations, learning model, evaluation, metrics, and implications of recent advances in deep learning architectures. re-identification. The briefed the models graphically along with the breakthroughs in DL research. Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Using recurrent neural networks for slot filling in spoken language understanding. Teaching machines to read and comprehend. Hinton and Salakhutdinov (2011) proposed a Deep Generative Model using Restricted Boltzmann Machines (RBM) for document processing. Jürgen Schmidhuber. share, Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation... m... Over the past few years, we have seen fundamental breakthroughs in core (2016a) proposed Recurrent Support Vector Machines (RSVM), which uses Re- current Neural Network (RNN) for extracting features from input sequence and standard Support Vector Machine (SVM) for sequence-level objective discrimination. Recent Advances in Hierarchical Reinforcement Learning Andrew G. Barto Sridhar Mahadevan Autonomous Learning Laboratory Department of Computer Science University of Massachusetts, Amherst MA 01003 Abstract Reinforcement learning is bedeviled by the curse of dimensionality: the number of parameters to be learned grows exponentially with the size of any compact encoding of a state. Dropout is a neural network model-averaging regularization method by adding noise to its hidden units. 03/26/2020 ∙ by Maithra Raghu, et al. Neural networks work with functionalities similar to human brain. (2016a) presented an experimental framework for understanding deep learning models. Redmon et al. Krueger et al. Ren et al. (2015) proposed Residual Networks (ResNets) consists of 152 layers. Fractals are repeated architecture generated by simple expansion rule (Larsson et al., 2016). Xie et al. (2014) proposed Dropout to prevent neural networks from overfitting. There are other issues like transferability of features learned (Yosinski et al., 2014). There are many rooms left for improvement. NTMs usually combine RNNs with external memory bank (Olah and Carter, 2016). This paper would be a good read to know the origin of the Deep Learning in evolutionary manner. (2017) presented overview on state-of-the-art of DL for remote sensing. Danilo Rezende, Shakir, Ivo Danihelka, Karol Gregor, and Daan Wierstra. Recent Advances in Deep Learning: An Overview. Yichuan Tang, Ruslan Salakhutdinov, and Geoffrey Hinton. This article reviews the recent advances in deep reinforcement learning with focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework. It is necessary to go through them for a DL researcher. (2014)), object detection (Lee et al. Krizhevsky et al. Information flow across several layers are called information highways (Srivastava et al., 2015). Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. We hope that this paper will help many novice researchers in this field, getting an overall picture of recent Deep Learning researches and techniques, and guiding them to the right way to start with. ∙ This article includes the basic idea of DL, major approaches and methods, recent breakthroughs and applications. A routing-by-agreement mechanism is used in these capsule lay- ers. (2017) proposed Multi-Expert Region-based Convolutional Neural Networks (ME R-CNN), which exploits Fast R-CNN (Girshick, 2015) architecture. In this section, we will briefly discuss about the deep neural networks (DNN), and recent improvements and breakthroughs of them. Then Support Vector Machine (SVM) surfaced, and surpassed ANNs for a while. When: 17th-18th September 2020. Girshick et al. Using a deep learning approach means leveraging massive volumes of training images in which different classes of objects, for example, cars or buildings, are labeled. Wang et al. Convolutional sequence to sequence learning. 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). Goodfellow et al. A routing-by-agreement mechanism is used in these capsule layers. Yangyang Shi, Kaisheng Yao, Hu Chen, Dong Yu, Yi-Cheng Pan, and Mei-Yuh Ioffe and Szegedy (2015) proposed Batch Normalization, a method for accelerating deep neural network training by reducing internal covariate shift. Lin et al. Deng and Yu (2014) provided detailed lists of DL applications in various categories e.g. Deng and Yu (2014) briefed deep architectures for unsupervised learning and explained deep Autoencoders in detail. Samira Shabanian, Devansh Arpit, Adam Trischler, and Yoshua Bengio. Ankit Kumar, Ozan Irsoy, Jonathan Su, James Bradbury, Robert English, Brian (2017) provided large-scale analysis of Vanilla LSTM and eight LSTM variants for three uses i.e. Many new techniques and architectures are invented, even after the most recently published overview paper on DL. Then, we will start describing the recent advances of this field. (2017) proposed Mask Region-based Convolutional Network (Mask R-CNN) instance object segmentation. They also pointed out the articles of major advances in DL in the bibliography. Recurrent neural networks with external memory for language Deep Q-Network (DQN), and applications in various fields. (2013),Mnih et al. Xie et al. Recent Advances in Deep Learning: An Overview. Deep learning methods are composed of multiple layers to learn features of data with multiple levels of abstraction (LeCun et al., 2015). Some more improvements proposed for GAN by Mao et al. (2017) proposed Variational Bi-LSTMs, which is a variant of Bidirectional LSTM architecture. provided detailed overview on the evolution and history of Deep Neural Networks (DNN) as well as Deep Learning (DL). However, there are many difficult problems for humanity to deal with. 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. The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Schmidhuber (2014) mentioned full history of neural networks from early neural networks to recent successful techniques. http://dl.acm.org/citation.cfm?id=3045390.3045543. Visualizing and understanding recurrent networks. Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, And fully-connected layers does the linear multiplication (Masci et al., 2013a). James Bradbury, Stephen Merity, Caiming Xiong, and Richard Socher. Start- ing from Machine Learning (ML) basics, pros and cons for deep architectures, they con- cluded recent DL researches and applications thoroughly. Marcus (2018) thinks DL needs to be reconceptualized and to look for possibilities in unsupervised learning, symbol manipulation and hybrid models, having insights from cognitive science and psychology and taking bolder challenges. (2016) proposed a DRL framework using asynchronous gradient descent for DNN optimization. For example, AlphaGo and AlphaGo Zero for game of GO (Silver et al. (2016) proposed a DRL framework using asynchronous gradient descent for DNN optimization. (2016) provided details of Recurrent and Recursive Neural Networks and architectures, its variants along with related gated and memory networks. We hope deep learning and AI will be much more devoted to the betterment of humanity, to carry out the hardest scientific researches, and last but not the least, to make the world a more better place for every single human. Daniel Holden, Jun Saito, and Taku Komura. (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). Fast R-CNN consists of convolutional and pooling layers, proposals of regions, and a sequence of fully connected layers (Girshick, 2015). Bahrampour et al. Bahrampour et al. 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.

recent advances in deep learning: an overview

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