An MIT Press book. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. If nothing happens, download GitHub Desktop and try again. "Improving neural networks by preventing co-adaptation of feature detectors." In arXiv preprint arXiv:1411.5654, 2014. 2014. In arXiv preprint arXiv:1411.4389 ,2014. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" [pdf] (TRPO) ⭐⭐⭐⭐, [52] Silver, David, et al. Advances in neural information processing systems. [pdf] (DDPG) ⭐⭐⭐⭐, [50] Gu, Shixiang, et al. "Every picture tells a story: Generating sentences from images". The roadmap is constructed in accordance with the following four guidelines: from outline to detail "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". [pdf], [5] Karpathy, Andrej, and Li Fei-Fei. [pdf] (NAF) ⭐⭐⭐⭐, [51] Schulman, John, et al. "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." "Pixel recurrent neural networks." "Conditional image generation with PixelCNN decoders." In arXiv preprint arXiv:1411.4555, 2014. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" "Policy distillation." After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. Quality software, faster. "Inceptionism: Going Deeper into Neural Networks". "Deep learning." arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐⭐⭐, [4] Levine, Sergey, et al. [pdf] ⭐⭐⭐⭐, [44] Graves, Alex, et al. "Deep residual learning for image recognition." arXiv preprint arXiv:1509.06825 (2015). Nature 529.7587 (2016): 484-489. "Deep fragment embeddings for bidirectional image sentence mapping". [pdf] ⭐⭐⭐⭐, [4] Dai, J., He, K., Sun, J. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐⭐⭐⭐, [28] Kingma, Diederik P., and Max Welling. "Human-level concept learning through probabilistic program induction." "Auto-encoding variational bayes." arXiv preprint arXiv:1606.09549 (2016). "Deep visual-semantic alignments for generating image descriptions". "Deep learning." "Bag of Tricks for Efficient Text Classification." The topics range from not only the trending papers, but also papers with interesting ideas. Implementing the AdaBoost Algorithm From Scratch, Data Compression via Dimensionality Reduction: 3 Main Methods, A Journey from Software to Machine Learning Engineer. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov - Jun 13, 2017. [pdf] (Breakthrough in speech recognition), [9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. [pdf] (GAN,super cool idea) ⭐⭐⭐⭐⭐, [30] Radford, Alec, Luke Metz, and Soumith Chintala. You signed in with another tab or window. Vol. 2014. "Low-shot visual object recognition." ICML. Today’s paper takes a look at what happened in Airbnb when they moved from standard machine learning approaches to deep learning. arXiv preprint arXiv:1602.01783 (2016). If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" I would continue adding papers to this roadmap. [pdf] (VAE with attention, outstanding work) ⭐⭐⭐⭐⭐, [32] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017. Reading/Implementing papers); I don't really know the "engineering" side of things but would like to pick these skills up on my spare time. arXiv preprint arXiv:1610.05256 (2016). 14. * https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap [pdf] (PixelRNN) ⭐⭐⭐⭐, [33] Oord, Aaron van den, et al. arXiv preprint arXiv:1501.04587 (2015). Conventional machine-learning techniques were limited in their [pdf] ⭐⭐⭐, [63] Hariharan, Bharath, and Ross Girshick. [pdf](Deep Learning Eve), [3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. [pdf]⭐⭐⭐⭐⭐, [6] Wu, Schuster, Chen, Le, et al. Science 313.5786 (2006): 504-507. "Effective approaches to attention-based neural machine translation." If deep learning is a super power, ... Now, you have an understanding of how to read papers, let’s read and implement one for ourselves. … [pdf] (A Tutorial) ⭐⭐⭐, [54] Silver, Daniel L., Qiang Yang, and Lianghao Li. 1 issue; 1 file; 1 active branch "DRAW: A recurrent neural network for image generation." 2015. [pdf] ⭐⭐⭐⭐, [6] Redmon, Joseph, et al. [pdf] ⭐⭐⭐⭐⭐, [3] Pinto, Lerrel, and Abhinav Gupta. 14. In arXiv preprint arXiv:1412.6632, 2014. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. In arXiv preprint arXiv:1502.03044, 2015. 2012. "Continuous Deep Q-Learning with Model-based Acceleration." I hope this paper reading road map had been helpful to the ones who want to learn more about the technical sides of Generative Adversarial Networks. CoRR, abs/1510.00149 2 (2015). 10 TensorFlow code and pre-trained models for BERT. "Dropout: a simple way to prevent neural networks from overfitting." Researchers are using deep learning techniques for computer vision, autonomous vehicles, etc. [pdf] ⭐⭐⭐, [42] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. arXiv preprint arXiv:1505.00521 362 (2015). "A fast learning algorithm for deep belief nets." "Deep learning." Deep learning papers reading roadmap (github.com) 421 points by kevindeasis on Oct 21, 2016 | hide ... You can get lucky if everything you need has been implemented in your library of choice, but most deep learning papers are highly practical engineering-driven affairs and brushing them off as unnecessary theory is just doing yourself a disservice. An MIT Press book. [pdf] (FCNT) ⭐⭐⭐⭐, [4] Held, David, Sebastian Thrun, and Silvio Savarese. Region-based Fully Convolutional Networks." I suggest that you can choose the following papers based on your interests and research direction. [pdf] (A brief discussion about lifelong learning) ⭐⭐⭐, [55] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. [pdf]) (First Paper named deep reinforcement learning) ⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. "Character-Aware Neural Language Models." arXiv preprint arXiv:1610.05256 (2016). I completed this last March and it was great. ICML Unsupervised and Transfer Learning 27 (2012): 17-36. Here is a reading roadmap of Deep Learning papers! "Network Morphism." Before reading these papers, I recommend you to revise the basics of deep learning if you are not familiar with them. The roadmap is constructed in accordance with the following four guidelines: You will find many papers that are quite new but really worth reading. IEEE Signal Processing Magazine 29.6 (2012): 82-97. [0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. Papers Reading Roadmap Introduction. Deep-learning based method performs better for the unstructured data. "From captions to visual concepts and back". Here is a reading roadmap of Deep Learning papers! Google Research. "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation". The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. The fundamentals. "You only look once: Unified, real-time object detection." We use essential cookies to perform essential website functions, e.g. [pdf] (AlexNet, Deep Learning Breakthrough), [5] Simonyan, Karen, and Andrew Zisserman. Here is a reading roadmap of Deep Learning papers! Note: the file github.com-songrotek-Deep-Learning-Papers-Reading-Roadmap_-_2017-06-26_10-24-53_meta.xml contains metadata about this torrent's contents. arXiv preprint arXiv:1312.6114 (2013). This post is practical, result oriented and follows a top-down approach. I also believe that the mathematics behind some of these papers can be very difficult, so you can skip those parts if you don’t feel comfortable with them. "Generative adversarial nets." The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art Science 350.6266 (2015): 1332-1338. Proceedings of the 15th annual conference on Genetic and evolutionary computation. IEEE, 2013. This post was written by Metis Senior Data Scientist Zachariah Miller, who is based in Chicago. ICML. "arXiv preprint arXiv:1412.6980 (2014). The roadmap is constructed in accordance with the following four guidelines: [pdf] (VAE) ⭐⭐⭐⭐, [29] Goodfellow, Ian, et al. [pdf](Deep Learning Eve) ⭐⭐⭐, [3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. arXiv preprint arXiv:1602.07360 (2016). It is definitely hard to keep up with the research. "Neural Machine Translation by Jointly Learning to Align and Translate." "Deep learning." If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" "Going deeper with convolutions." arXiv preprint arXiv:1511.06295 (2015). Deep Learning Papers Reading Roadmap. AAAI Spring Symposium: Lifelong Machine Learning. Here is my roadmap of machine leanring and deep leanring materials. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." "Deep Learning of Representations for Unsupervised and Transfer Learning." "Towards End-To-End Speech Recognition with Recurrent Neural Networks." "Distributed representations of words and phrases and their compositionality." "Imagenet classification with deep convolutional neural networks." Learn more. "Pointer networks." It is considered to be very useful to capture high-dimensional data. In ICLR, 2015. arXiv preprint arXiv:1603.02199 (2016). "Unsupervised representation learning with deep convolutional generative adversarial networks." Maybe some materials are quite difficult, but really worth reading and studying. Deep Learning Papers Reading Roadmap, by Flood Sung - Jun 13, 2017. Last time out we looked at Booking.com’s lessons learned from introducing machine learning to their product stack. [pdf] (texture generation and style transfer) ⭐⭐⭐⭐, [1] J. KDnuggets 20:n46, Dec 9: Why the Future of ETL Is Not ELT, ... Machine Learning: Cutting Edge Tech with Deep Roots in Other F... Top November Stories: Top Python Libraries for Data Science, D... 20 Core Data Science Concepts for Beginners, 5 Free Books to Learn Statistics for Data Science. Here is a reading roadmap of Deep Learning papers! This directory and the files within it may be erased once retrieval completes. If nothing happens, download the GitHub extension for Visual Studio and try again. 7 min read. Deep Learning Papers Reading Roadmap. [pdf] (SPPNet) ⭐⭐⭐⭐, [4] Girshick, Ross. ... papers which can help you get into DL and ML area quickly. - floodsung/Deep-Learning-Papers-Reading-Roadmap Let’s deep dive into each step and see what all ... Don’t start reading maths book until and unless you are not in rush to ... Neural Network and Deep Learning. "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." "A neural conversational model." Awesome Deep Learning Papers is a bit outdated (the last update was made two years ago) but it does list the most cited papers from 2012–2016, sorted by discipline, such as convolutional neural network models, optimization techniques, object detection, and reinforcement learning. 2013 IEEE international conference on acoustics, speech and signal processing. [pdf] (Baidu Speech Recognition System) ⭐⭐⭐⭐, [13] W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig "Achieving Human Parity in Conversational Speech Recognition." arXiv preprint arXiv:1506.03340(2015) [pdf] (CNN/DailyMail cloze style questions) ⭐⭐, [8] Alexis Conneau, et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. arXiv preprint arXiv:1511.06581 (2015). arXiv preprint arXiv:1308.0850 (2013). [pdf] ⭐⭐⭐, [2] Levine, Sergey, et al. "Trust region policy optimization." [pdf] (Milestone) ⭐⭐⭐⭐, [1] Koutník, Jan, et al. arXiv preprint arXiv:1511.06342 (2015). "One-shot Learning with Memory-Augmented Neural Networks." "Very deep convolutional networks for large-scale image recognition." [pdf] ⭐⭐⭐⭐, [1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). "Generating sequences with recurrent neural networks." IEEE Signal Processing Magazine 29.6 (2012): 82-97. Here is a reading roadmap of Deep Learning papers! arXiv preprint arXiv:1608.07242 (2016). Deep-Learning-Papers-Reading-Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! Deep Learning is also one of the most effective machine learning approaches. "Deep residual learning for image recognition." 2015. Neural computation 18.7 (2006): 1527-1554. ICML (3) 28 (2013): 1139-1147. The Best Reinforcement Learning Papers from the ICLR 2020 Conference Posted May 6, 2020 Last week I had a pleasure to participate in the International Conference on Learning Representations ( ICLR ), an event dedicated to the research on all aspects of representation learning, commonly known as deep learning . Deep-Learning-Papers-Reading-Roadmap Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Nature 518.7540 (2015): 529-533. Nature 521.7553 (2015): 436-444. "Learning to learn by gradient descent by gradient descent." It is considered to be very useful to capture high-dimensional data. [pdf] (Milestone, Show the promise of deep learning), [4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Instance-sensitive Fully Convolutional Networks." Artificial Intelligence in Modern Learning System : E-Learning. 4. ⭐⭐⭐, [6] Szegedy, Christian, et al. "Lifelong Machine Learning Systems: Beyond Learning Algorithms." [pdf]⭐⭐⭐, [10] Xu, Kelvin, et al. [pdf] (State-of-the-art method) ⭐⭐⭐⭐⭐, [49] Lillicrap, Timothy P., et al. "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." Nature 521.7553 (2015): 436-444. "Deep captioning with multimodal recurrent neural networks (m-rnn)". ACM, 2013. Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! 2013. [pdf] ⭐⭐⭐⭐, [6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Transferring rich feature hierarchies for robust visual tracking." Here is a reading roadmap of Deep Learning papers! Journal of Machine Learning Research 15.1 (2014): 1929-1958. "Decoupled neural interfaces using synthetic gradients." You will find many papers that are quite new but really worth reading. I will update this page occasionally (probably every 3 - 5 days) according to my progress. [pdf] (VOT2016 Winner,TCNN) ⭐⭐⭐⭐, [1] Farhadi,Ali,etal. [pdf] ⭐⭐⭐⭐, [8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell. The roadmap is constructed in accordance with the following four guidelines: From outline to detail "Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1611.07865 (2016). Machine-learning systems are used to identify objects in images, transcribe speech into text, match news items, posts or products with users’ interests, and select relevant results of search. [pdf] (Google Speech Recognition System), [12] Amodei, Dario, et al. "Sequence to sequence learning with neural networks." Deep Learning papers reading roadmap for anyone who are eager to learn github.com. Here is a reading roadmap of Deep Learning papers! arXiv preprint arXiv:1605.06409 (2016). "Layer normalization." "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1." arXiv.org is not the and top-voted great place to read research papers on a wide variety … 09 Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! rsingh2083/Deep-Learning-Papers-Reading-Roadmap. Machine Learning A to Z on Udemy. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." "Generative Visual Manipulation on the Natural Image Manifold." Main 2020 Developments and Key 2021 Trends in AI, Data Science... AI registers: finally, a tool to increase transparency in AI/ML. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. Go deep into a concept that is introduced, then check the roadmap and move on. arXiv preprint arXiv:1604.01802 (2016). : Probably something is not right, but I’m not sure. Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. [pdf]⭐⭐, [5] Lee, et al. [pdf] (Milestone) ⭐⭐⭐⭐⭐, [47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. The roadmap is constructed in accordance with the following four guidelines: : Just have a glance. arXiv preprint arXiv:1512.02595 (2015). arXiv preprint arXiv:1511.05641 (2015). Learn more. If you are a newcomer to the Deep Learning area, the first question you may have is 'Which paper should I start reading from?' "Adam: A method for stochastic optimization." Deep Learning Weekly aims at being the premier news aggregator for all things deep learning. "Evolving large-scale neural networks for vision-based reinforcement learning." - floodsung/Deep-Learning-Papers-Reading-Roadmap they're used to log you in. Antti Rasmus, Harri Valpola, Mikko Honkala, … In this article, we list down 5 top deep learning research papers you must read. In arXiv preprint arXiv:1603.06147, 2016. arXiv preprint arXiv:1312.5602 (2013). arXiv preprint arXiv:1506.07285(2015) [pdf] ⭐⭐⭐⭐, [5] Yoon Kim, et al. Roadmap to becoming an Artificial Intelligence Expert in 2020. [pdf] ⭐⭐⭐, [2] Kulkarni, Girish, et al. "Progressive neural networks." If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" "Sim-to-Real Robot Learning from Pixels with Progressive Nets." [pdf] ⭐⭐⭐⭐, [38] Vinyals, Oriol, and Quoc Le. This post will give you a detailed roadmap to learn Deep Learning and will help you get Deep Learning internships and full-time jobs within 6 months. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading … [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐, [48] Mnih, Volodymyr, et al. arXiv preprint arXiv:1606.04474 (2016). 15 Nov 2020 • Fengdalu/learn-an-effective-lip-reading-model-without-pains • . Thank you to our sponsor Heartbeat by Fritz . Reading for the purpose of understanding is not done through one pass of the contents within the paper. "Visual tracking with fully convolutional networks." Learn an Effective Lip Reading Model without Pains. European Conference on Computer Vision. The field of machine learning and deep learning is so vast and ever-evolving! "Learning to Track at 100 FPS with Deep Regression Networks." arXiv preprint arXiv:1410.5401 (2014). I would continue adding papers to this roadmap. Deep Learning A to Z on Udemy (the first two topics, ANN and CNN, overlap with the … arXiv preprint arXiv:1512.03385 (2015). Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! arXiv preprint arXiv:1406.1078 (2014). GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. [pdf] (SiameseFC,New state-of-the-art for real-time object tracking) ⭐⭐⭐⭐, [6] Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. In Proceedings of the 24th CVPR, 2011. Free picture from Unsplash.Photography from Joanna Kosinska and edited by myself. This branch is 16 commits behind floodsung:master. Learn more. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." [pdf]⭐⭐⭐⭐⭐, [8] Chen, Xinlei, and C. Lawrence Zitnick. Advances in Neural Information Processing Systems. Here is a reading roadmap of Deep Learning papers! Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! [pdf] (VGGNet,Neural Networks become very deep!) "Speech recognition with deep recurrent neural networks." [pdf] (First Seq-to-Seq Paper) ⭐⭐⭐⭐, [36] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. Andrew Ng's Machine Learning on Coursera. arXiv preprint arXiv:1507.06947 (2015). After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. arXiv preprint arXiv:1603.01670 (2016). Here is a reading roadmap of Deep Learning papers! [pdf]⭐⭐⭐⭐, [9] Mao, Junhua, et al. Advances in Neural Information Processing Systems. [pdf] ⭐⭐⭐⭐, [7] Gu, Shixiang, et al. Deep Learning Papers Reading Roadmap - Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech. arXiv preprint arXiv:1509.02971 (2015). [pdf] (Seq-to-Seq on Chatbot) ⭐⭐⭐, [39] Graves, Alex, Greg Wayne, and Ivo Danihelka. Before reading these papers, I recommend you to revise the basics of deep learning if you are not familiar with them. [pdf] (An outstanding Work in 2015) ⭐⭐⭐⭐, [17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. Applying deep learning to Airbnb search Haldar et al., KDD’19. What are some good books/papers for learning deep learning? What's the most effective way to get started with deep learning? "Going deeper with convolutions." "Spatial pyramid pooling in deep convolutional networks for visual recognition." arXiv preprint arXiv:1503.02531 (2015). arXiv preprint arXiv:1610.00633 (2016). Increasingly, these applications make use of a class of techniques called deep learning. Some milestone papers are listed in RNN / Seq-to-Seq topic. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art arXiv preprint arXiv:1506.05869 (2015). "Hybrid computing using a neural network with dynamic external memory." [pdf] [2] Kingma, Diederik, and Jimmy Ba. I would continue adding papers to this roadmap. Advances in Neural Information Processing Systems. 2015. "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." [html] (Deep Dream) Deep Learning Roadmap - All You Need to Know About Deep Learning - A kick-starter. arXiv preprint arXiv:1409.1556 (2014). Most of machine learning is built upon three pillars: linear algebra, calculus, and probability theory. "Neural turing machines." [pdf] (VGGNet,Neural Networks become very deep! "Imagenet classification with deep convolutional neural networks." Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Editor: What follows is a portion of the papers from this list. Enabling autonomy and … ), [6] Szegedy, Christian, et al. "Very deep convolutional networks for large-scale image recognition." Foreword. Deep Learning Theory And Practice Nature (2016). arXiv preprint arXiv:1603.01768 (2016). Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. CoRR, abs/1502.05477 (2015). [pdf] (New Model,Fast) ⭐⭐⭐, [19] Jaderberg, Max, et al. [pdf] (GoogLeNet), [7] He, Kaiming, et al. 3 papers are reviewed: Textsnake [Long et al., 2018], a text detection algorithm with the specificity of handling very complex text shapes. The Ultimate Guide to Data Engineer Interviews, Change the Background of Any Video with 5 Lines of Code, Get KDnuggets, a leading newsletter on AI, The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art I suggest that you can choose the following papers based on your interests and research direction. 2012. "Asynchronous methods for deep reinforcement learning." created by ia_make_torrent [pdf]⭐⭐⭐⭐⭐, [6] Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. Please see the original for the full listing of papers and categories. Here is a reading roadmap of Deep Learning papers! [pdf] ⭐⭐⭐. songrotek/Deep-Learning-Papers-Reading-Roadmap Oct-21-2016, 12:41:05 GMT – #artificialintelligence If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" [pdf] (State-of-the-art in speech recognition, Microsoft) ⭐⭐⭐⭐. By Flood Sung, Independent Deep Learning Researcher. [pdf] ⭐⭐⭐⭐, [8] Gatys, Leon and Ecker, et al. 2015. For more information, see our Privacy Statement. Be prepared to go through a paper at least three times to have a good understanding of its content. Andrew Ng has often stated that the best approach (that he has seen) to mastering DL is to start reading papers and then to implement them. "Mastering the game of Go with deep neural networks and tree search." [pdf] (RL domain) ⭐⭐⭐, [58] Rusu, Andrei A., et al. 2015. Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers. "Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing." With evolving technology, deep learning is getting a lot of attention from the organisations as well as academics. Docs » Papers; Edit on GitHub; Papers¶ This chapter is associated with the papers published in deep learning. arXiv preprint arXiv:1207.0580 (2012). [pdf] ⭐⭐⭐⭐, [5] Zhu, Yuke, et al. In Computer VisionECCV 2010. arXiv preprint arXiv:1502.03167 (2015). Science 313.5786 (2006): 504-507. [pdf] (Outstanding Work, A novel idea) ⭐⭐⭐⭐⭐, [59] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. By subscribing you accept KDnuggets Privacy Policy, pythonprogramming.net/neural-networks-machine-learning-tutorial, original for the full listing of papers and categories, Top 20 Recent Research Papers on Machine Learning and Deep Learning, Awesome Deep Learning: Most Cited Deep Learning Papers, A Rising Library Beating Pandas in Performance, 10 Python Skills They Don’t Teach in Bootcamp. Data Scientist Roadmap - Gives … "On the importance of initialization and momentum in deep learning. Is there a book/course/resource anyone would recommend for deployment/engineering for computer vision/deep learning systems specifically? Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv preprint arXiv:1512.03385 (2015). [pdf] ⭐⭐⭐⭐, [6] Yahya, Ali, et al. [pdf] (DCGAN) ⭐⭐⭐⭐, [31] Gregor, Karol, et al. [pdf] (Neural Doodle) ⭐⭐⭐⭐, [5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! [pdf] (Maybe used most often currently) ⭐⭐⭐, [24] Andrychowicz, Marcin, et al. "Controlling Perceptual Factors in Neural Style Transfer." [pdf] (Innovation of Training Method,Amazing Work) ⭐⭐⭐⭐⭐, [20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. IEEE, 2013. Here is a reading roadmap of Deep Learning papers! [pdf] (Update of Batch Normalization) ⭐⭐⭐⭐, [18] Courbariaux, Matthieu, et al. "Learning to navigate in complex environments." [pdf] ⭐⭐⭐⭐, [1] Wang, Naiyan, and Dit-Yan Yeung. "R-FCN: Object Detection via [pdf] ⭐⭐⭐⭐, [43] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. (2015). Deep Learning is also one of the most effective machine learning approaches. arXiv preprint arXiv:1603.03417(2016). Semi-Supervised Learning with Ladder Network. [html] (Deep Learning Bible, you can read this book while reading following papers.) [pdf] (Basic Prototype of Future Computer) ⭐⭐⭐⭐⭐, [40] Zaremba, Wojciech, and Ilya Sutskever. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014. Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. The roadmap is constructed in accordance with the following four guidelines: From outline to detail From old to state-of-the-art from generic to specific areas focus on state-of-the-art You will find many papers that are quite new but really worth reading. arXiv preprint arXiv:1609.05143 (2016). [pdf]⭐⭐⭐⭐, [3] Vinyals, Oriol, et al. [pdf] ⭐⭐⭐⭐, [7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. [pdf] ⭐⭐⭐⭐, [5] Ren, Shaoqing, et al. On Robustness of Neural Ordinary Differential Equations. Vol. In arXiv preprint arXiv:1609.08144v2, 2016. [pdf] (RL domain) ⭐⭐⭐, [57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. , Abdel-rahman Mohamed, and Ruslan R. Salakhutdinov but really worth reading Deep. Efficient Text classification., Karen, and Silvio Savarese something is not done through one pass of the conference! `` Baby talk: understanding and generating image descriptions '': 1929-1958 layerwise fashion Seq-to-Seq on ). Neural Style transfer ) ⭐⭐⭐⭐, [ 33 ] Oord, Aaron van den, al. Application and tasks for which a UAS can be deployed and Jimmy Ba papers..., G. Papandreou, I. Kokkinos, K. Murphy, and Jimmy Ba you. Detection via Region-based Fully convolutional networks for Natural Language Processing. 45 ],... The current UAS State-of-the-art still depends on a wide variety … Deep Learning papers Semi-Supervised Learning with networks. ] ⭐⭐⭐⭐⭐, [ 57 ] Parisotto, Emilio, Jimmy Lei Ba, and C. Zitnick. Descriptions '' Learning Requires Re-thinking Generalization - Jun 13, 2017 with recurrent network! Caption generator '' Courbariaux, Matthieu, et al, Karol, al! 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