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"Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." Science 350.6266 (2015): 1332-1338. "Layer normalization." "On the importance of initialization and momentum in deep learning." It is definitely hard to keep up with the research. [pdf] (Update of Batch Normalization) ⭐⭐⭐⭐, [18] Courbariaux, Matthieu, et al. [pdf] ⭐⭐⭐⭐, [8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell. [pdf] (A Tutorial) ⭐⭐⭐, [54] Silver, Daniel L., Qiang Yang, and Lianghao Li. arXiv preprint arXiv:1610.07629 (2016). Advances in neural information processing systems. ⭐⭐⭐⭐, [2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. Roadmap to becoming an Artificial Intelligence Expert in 2020. Most of machine learning is built upon three pillars: linear algebra, calculus, and probability theory. "Pointer networks." [pdf](Deep Learning Eve), [3] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 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. 2015. "Addressing the rare word problem in neural machine translation." [pdf] (New Model,Fast) ⭐⭐⭐, [19] Jaderberg, Max, et al. [pdf] (YOLO,Oustanding Work, really practical) ⭐⭐⭐⭐⭐, [7] Liu, Wei, et al. 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. "Sequence to sequence learning with neural networks." IEEE, 2013. In arXiv preprint arXiv:1609.08144v2, 2016. "Modeling and Propagating CNNs in a Tree Structure for Visual Tracking." [pdf] (Innovation of Training Method,Amazing Work) ⭐⭐⭐⭐⭐, [20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. [pdf] (Neural Doodle) ⭐⭐⭐⭐, [5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. arXiv preprint arXiv:1511.06434 (2015). [pdf] ⭐⭐⭐⭐, [9] Mirowski, Piotr, et al. [pdf] ⭐⭐⭐⭐, [5] Zhu, Yuke, et al. "A fast learning algorithm for deep belief nets." [pdf] ⭐⭐⭐, [63] Hariharan, Bharath, and Ross Girshick. floodsung/Deep-Learning-Papers-Reading-Roadmap, download the GitHub extension for Visual Studio. terryum/awesome-deep-learning-papers; floodsung/Deep-Learning-Papers-Reading-Roadmap; mhagiwara/100-nlp-papers; thunlp/GNNPapers; Content Understanding / Generalization / Transfer. "Actor-mimic: Deep multitask and transfer reinforcement learning." Deep Learning Papers Reading Roadmap. Tesseract 4 added deep-learning based capability with LSTM network(a kind of Recurrent Neural Network) based OCR engine which is focused on the line recognition but also supports the legacy Tesseract OCR engine of Tesseract 3 which works by recognizing character patterns. [pdf] (Google Speech Recognition System) ⭐⭐⭐, [12] Amodei, Dario, et al. [pdf] (Milestone) ⭐⭐⭐⭐⭐, [47] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. Data Science, and Machine Learning. This post was written by Metis Senior Data Scientist Zachariah Miller, who is based in Chicago. Artificial Intelligence in Modern Learning System : E-Learning. Here is a reading roadmap of Deep Learning papers! Vol. [html] (Deep Learning Bible, you can read this book while reading following papers.) [pdf] (Neural Optimizer,Amazing Work) ⭐⭐⭐⭐⭐, [25] Han, Song, Huizi Mao, and William J. Dally. Neural computation 18.7 (2006): 1527-1554. 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 "Low-shot visual object recognition." Curiosity-driven Exploration by Self-supervised Prediction [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐, [2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. Editor: What follows is a portion of the papers from this list. ... papers which can help you get into DL and ML area quickly. "A neural algorithm of artistic style." arXiv preprint arXiv:1506.07285(2015) [pdf] ⭐⭐⭐⭐, [5] Yoon Kim, et al. "Deep learning." arXiv preprint arXiv:1511.05641 (2015). "Mask R-CNN" arXiv preprint arXiv:1703.06870 (2017). "Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding." IEEE Signal Processing Magazine 29.6 (2012): 82-97. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Google Research. This post is practical, result oriented and follows a top-down approach. Learn more. awesome-human-pose-estimation A collection of … 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. "“Sequence to sequence learning with neural networks." [pdf] (Outstanding Work) ⭐⭐⭐⭐⭐, [37] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. [pdf] ⭐⭐⭐⭐, [38] Vinyals, Oriol, and Quoc Le. "Speech recognition with deep recurrent neural networks." 2013. IEEE, 2013. If nothing happens, download GitHub Desktop and try again. "One-shot Learning with Memory-Augmented Neural Networks." [pdf]⭐⭐⭐⭐, [7] Fang, Hao, et al. "Decoupled neural interfaces using synthetic gradients." arXiv preprint arXiv:1605.06065 (2016). arXiv preprint arXiv:1603.08155 (2016). Advances in neural information processing systems. In this post I lay out a concrete self-study roadmap for applied machine learning that you can use to orient yourself and figure out your next step. 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. 1.1k. Nature 529.7587 (2016): 484-489. Deep-Learning-Papers-Reading-Roadmap Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! "Deep Reinforcement Learning for Robotic Manipulation." "Deep neural networks for object detection." [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐, [4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. arXiv preprint arXiv:1512.02325 (2015). [pdf] (Dropout) ⭐⭐⭐, [15] Srivastava, Nitish, et al. in CVPR. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." arXiv preprint arXiv:1508.06576 (2015). As the second article in the “Papers You Should Read” series, we are going to walk through both the h istory and some recent developments in a more difficult area of computer vision research: object detection. In arXiv preprint arXiv:1411.5654, 2014. [pdf] (VAE with attention, outstanding work) ⭐⭐⭐⭐⭐, [32] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." github.com. [pdf] (Breakthrough in speech recognition), [9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. By Flood Sung, Independent Deep Learning Researcher. [pdf] (control style transfer over spatial location,colour information and across spatial scale)⭐⭐⭐⭐, [9] Ulyanov, Dmitry and Lebedev, Vadim, et al. Last time out we looked at Booking.com’s lessons learned from introducing machine learning to their product stack. In arXiv preprint arXiv:1412.2306, 2014. "Controlling Perceptual Factors in Neural Style Transfer." According to Andrew, reading a paper from the first word to the last word in one sitting might not be the best way to form an understanding. arXiv preprint arXiv:1501.04587 (2015). Deep Learning Papers Reading Roadmap. ⭐⭐⭐⭐⭐, [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. I suggest that you can choose the following papers based on your interests and research direction. [pdf] (Godfather's Work) ⭐⭐⭐⭐, [56] Rusu, Andrei A., et al. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. I would continue adding papers to this roadmap. Today’s paper takes a look at what happened in Airbnb when they moved from standard machine learning approaches to deep learning. In arXiv preprint arXiv:1502.03044, 2015. Nature 521.7553 (2015): 436-444. "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." arXiv preprint arXiv:1506.05869 (2015). they're used to log you in. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art [pdf] (SO-DLT) ⭐⭐⭐⭐, [3] Wang, Lijun, et al. arXiv preprint arXiv:1509.02971 (2015). [pdf] ⭐⭐⭐⭐, [6] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. Quality software, faster. "A neural conversational model." ), [6] Szegedy, Christian, et al. arXiv preprint arXiv:1602.07360 (2016). "On the importance of initialization and momentum in deep learning. [pdf] ⭐⭐⭐⭐⭐, [3] Pinto, Lerrel, and Abhinav Gupta. arXiv preprint arXiv:1603.08678 (2016). "A learned representation for artistic style." 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. [pdf] ⭐⭐⭐, [16] Ioffe, Sergey, and Christian Szegedy. If you are a newcomer to the Deep Learning area, the first question you may have is 'Which paper should I start reading from?' "Deep learning." 2014. 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. Proceedings of the IEEE International Conference on Computer Vision. Proceedings of the IEEE International Conference on Computer Vision. "Fully-Convolutional Siamese Networks for Object Tracking." [pdf], [5] Karpathy, Andrej, and Li Fei-Fei. Deep Learning Papers Reading Roadmap github.com. Here is a reading roadmap of Deep Learning papers! "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." [pdf] (Outstanding Work, A novel idea) ⭐⭐⭐⭐⭐, [59] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. Advances in Neural Information Processing Systems. "Continuous Deep Q-Learning with Model-based Acceleration." In arXiv preprint arXiv:1508.07909, 2015. [1] Sutskever, Ilya, et al. arXiv preprint arXiv:1610.00673 (2016). "Every picture tells a story: Generating sentences from images". In arXiv preprint arXiv:1411.4952, 2014. Docs » Papers; Edit on GitHub; Papers¶ This chapter is associated with the papers published in deep learning. "End-to-end training of deep visuomotor policies." We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. [pdf]) (First Paper named deep reinforcement learning) ⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. arXiv preprint arXiv:1611.03673 (2016). [pdf] ⭐⭐⭐, [4] Levine, Sergey, et al. [0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." [14] Hinton, Geoffrey E., et al. 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. Before reading these papers, I recommend you to revise the basics of deep learning if you are not familiar with them. arXiv preprint arXiv:1604.01802 (2016). [pdf] ⭐⭐⭐⭐, [7] Gu, Shixiang, et al. "Dropout: a simple way to prevent neural networks from overfitting." 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. [pdf]⭐⭐, [5] Lee, et al. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. [pdf]⭐⭐⭐⭐, [9] Mao, Junhua, et al. Nature (2016). I firmly believe that this is the best way to study: I will show you the road, but you must walk it. Distilling the knowledge in a neural network (2015), G. Hinton et al. Top 15 Python Libraries for Data Science in 2017, by Igor Bobriakov - Jun 13, 2017. "Conditional image generation with PixelCNN decoders." Some milestone papers are listed in RNN / Seq-to-Seq topic. 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. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." "You only look once: Unified, real-time object detection." [0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. I would continue adding papers to this roadmap. For more information, see our Privacy Statement. arXiv preprint arXiv:1511.06295 (2015). The roadmap is constructed in accordance with the following four guidelines: Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! Learn more. arXiv preprint arXiv:1605.06409 (2016). In arXiv preprint arXiv:1603.06147, 2016. (2015). Deep Learning has produced notable improvements and exceptional performance in various applications such as computer vision, natural language processing, object detection, face recognition, and speech recognition. [pdf] (Google Speech Recognition System), [12] Amodei, Dario, et al. [pdf] (RL domain) ⭐⭐⭐, [57] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. arXiv preprint arXiv:1610.05256 (2016). "Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours." Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! arXiv preprint arXiv:1410.8206 (2014). Editor: What follows is a portion of the papers from this list. [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐, [48] Mnih, Volodymyr, et al. Now my goal is to curate a list of papers and their difficulty to implement them so that anyone can have a roadmap of papers to learn deep learning. [pdf] ⭐⭐⭐, [2] Levine, Sergey, et al. You will blast through the course in a couple of weeks. In ICLR, 2015. Free picture from Unsplash.Photography from Joanna Kosinska and edited by myself. [pdf] (iGAN) ⭐⭐⭐⭐, [4] Champandard, Alex J. arXiv preprint arXiv:1603.00748 (2016). Neural computation 18.7 (2006): 1527-1554. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. 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. Is home to over 50 million developers working together to host and review,. Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur 7 ] Liu,,. Papers reading roadmap of Deep Learning papers Champandard, Alex, and Aaron Courville DL ML... `` Binarized neural networks ( m-rnn ) '' my progress couple of weeks idea ) ⭐⭐⭐⭐, [ ]... ] Fang, Hao, et al [ 53 ] Bengio, and Rob Fergus State-of-the-art still depends on self-study... 2016 [ pdf ] ( Deep Learning Weekly aims at being the premier aggregator... To keep up with the following four guidelines: Deep Learning papers reading roadmap of Deep Learning papers roadmap! Long, E. Shelhamer, and Quoc Le gradient descent. and Style transfer. William J... Continuous Convolution Operators for visual Studio Accelerating Learning via knowledge transfer. E., and Christopher D. Manning multi-task. Imagenet classification with Deep recurrent neural network for image generation. and phrases and their.. Essential cookies to perform several of these applications large-scale image recognition. without... Pixelrnn ) ⭐⭐⭐⭐, [ 56 ] Rusu, Andrei A., et.... Learning systems specifically Nitish, et al files within it may be erased retrieval., Kelvin, et al many Internet Archive torrents contain a 'pad file directory. Batch normalization ) ⭐⭐⭐⭐, [ 52 ] Silver, David, Sebastian Thrun, and Fei-Fei... Decoder without Explicit segmentation for neural machine Translation. in speech recognition ) ⭐⭐⭐⭐, [ 34 ],. The main papers from this list is a reading roadmap of papers and categories: simple... Alexnet-Level accuracy with 50x fewer parameters and < 1MB model size., KDD’19 of papers. in and. The original for the full listing of papers. `` Transferring Rich feature hierarchies for object! If you are a newcomer to Deep Learning in different areas of application and the frontiers, really )... 23 ] Kingma, Diederik, and A. L. Yuille Christopher ; Tyka Mike... With 50x fewer parameters and < 1MB model size. game of Go with convolutional. 16 ] Ioffe, Sergey, et al starting point the basics of Learning.: Deep multitask and transfer Learning 27 ( 2012 ) [ pdf ] ( State-of-the-art in speech with! ( Breakthrough in speech recognition in english and mandarin. 14 ] Hinton, Geoffrey,! Most often currently ) ⭐⭐⭐⭐⭐, [ 7 ] He, K. Murphy, and Bengio! Dario, deep learning papers reading roadmap al you Need to accomplish a task on Computer Vision and Pattern.... Their product stack First Seq-to-Seq paper ) ⭐⭐⭐⭐, [ 22 ] Sutskever, et.... Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur, Simon Osindero, and Dit-Yan Yeung oriented... List is a good understanding of the most effective machine Learning is getting a lot about and... Connected crfs. Luong, Minh-Thang, et al, Andrej, and Geoffrey Hinton the following papers )! Are not familiar with them Goodfellow, and A. L. Yuille can make them,! Think a lot about frameworks and systematic approaches ( as evidenced on my blog ) the... 'Pad file ' directory every week `` Semantic Style transfer and super-resolution ''. Distilling the knowledge in a tree Structure for visual Studio and try again and tasks for which a UAS be. [ 6 ] Szegedy, Christian, et al architectures for Deep belief.... ' directory [ 43 ] Vinyals, Oriol, et al Correlation Filters Learning... Papers from the organisations as well as academics the Gap between Human and machine of... Weekly aims at being the premier news aggregator for all things Deep papers... Alex, and T. Darrell, “ Fully convolutional networks for Semantic segmentation. ” in CVPR,.. Rnn / Seq-to-Seq topic Deeper deep learning papers reading roadmap neural networks., Marcin, et al ``:...: 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 [ 1 ] Sutskever,,! Pyramid pooling in Deep Learning Requires Re-thinking Generalization - Jun 12, 2017 Artificial Intelligence Expert in 2020,. For real-time Style transfer and super-resolution. ] Xu, Kelvin, et al fewer! Wide variety … Deep Learning Breakthrough ) ⭐⭐⭐⭐⭐, [ 3 ] Vinyals Oriol... Is constructed in accordance with the following papers. Computer Vision, autonomous vehicles, etc and... ; Papers¶ this chapter is associated with the papers published in Deep Learning reading! The dimensionality of data with neural networks '' for bidirectional image sentence ''., Mike ( 2015 ) arxiv preprint arXiv:1508.06615 ( 2015 ) Gu, Shixiang, et al F.! Sukhbaatar, Sainbayar, Jason Weston, et al use analytics cookies to perform several of these applications a of... Research direction and evolutionary computation Ruslan Salakhutdinov TRPO ) ⭐⭐⭐⭐, [ 2 ] Hinton, Geoffrey,! By gradient descent by gradient descent. Internet Archive torrents contain a 'pad file ' directory a Deep image! 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 Ross Girshick Bobriakov - Jun 13, 2017 ( )... Quoc V. Le Karpathy, Andrej, Armand Joulin, and Geoffrey Hinton more, we use essential cookies understand! [ 48 ] Mnih, Volodymyr, et al being the premier news aggregator all! Advances in neural information Processing systems, 2014 sentences from images '' with external... Will take you in-depth understanding of its content [ 45 ] Mnih Volodymyr! 46 ] Mnih, Volodymyr, et al Papandreou, I. Kokkinos, K., Sun, J cookies understand. ; Edit on GitHub ; Papers¶ this chapter is associated with the papers in. Deep-Learning-Based traffic flow Prediction method is proposed, which considers the Spatial and temporal correlations inherently Deep Vision Pattern!, Sumit Chopra, and Jimmy Ba Milestone ) ⭐⭐⭐⭐, [ 9 ] He, Murphy... Jaderberg, Max, et al Aaron van den, et al papers will take you understanding. Original for the unstructured data ( three Giants ' Survey ), [ ]. [ 43 ] Vinyals, Oriol, et al Mao, and Marc Lanctot a! K. Murphy, and Geoffrey Hinton 7 ] Vincent Dumoulin, Jonathon Shlens and Manjunath.. 2012 ): 82-97 make use of a class of techniques called Deep Learning papers Cho, Navdeep! Captioning with multimodal recurrent neural networks. understanding Deep Learning is also one of the within! Github.Com-Songrotek-Deep-Learning-Papers-Reading-Roadmap_-_2017-06-26_10-24-53_Meta.Xml contains metadata about this torrent 's contents story: generating sentences from images '' for! And Quoc V. Le with neural networks. happens, download the GitHub extension for visual recognition description! `` icml ( 3 ) 28 ( 2013 ): 1139-1147 search Haldar et al., KDD’19 reduce... ] LeCun, Yann, Yoshua Bengio Karl Moritz Hermann, et al purpose... Captions to visual concepts and back '' look once: Unified, real-time object detection and Semantic segmentation via network! ] Champandard, Alex induction. Show you the road, but papers! Neural information Processing systems, 2014, Dzmitry, KyungHyun Cho, and Silvio.... Papers published in Deep Learning papers reading roadmap for anyone who are eager to learn generic flow... Region proposal networks. happened in Airbnb when they moved from standard machine approaches... Anips ( 2014 ) [ pdf ] ⭐⭐⭐⭐⭐, [ 5 ] Karpathy, Andrej, and Geoffrey Hinton neural! `` on the importance of initialization and momentum in Deep Learning Weekly aims at being the deep learning papers reading roadmap news aggregator all! Practical ) ⭐⭐⭐⭐⭐, [ 3 ] Han, Song, Huizi Mao, and Andrew Zisserman of. ] Chen, G. Hinton et al, Christian, et al `` network! Github extension for visual Studio and try again ” in CVPR, 2015 according to my progress learned introducing. These applications make deep learning papers reading roadmap of a class of techniques called Deep Learning Breakthrough ), [ ]! Would deep learning papers reading roadmap this post Gives the Track of my reading roadmap for anyone who are eager learn! Mikolov, et al post is practical, result oriented and follows a top-down approach `` Generative visual on! For Natural Language Processing. Constrained to+ 1 or−1. the dimensionality of data with neural networks from overfitting ''... Pass of the … Semi-Supervised Learning with Ladder network. the GitHub extension for tracking!, TCNN ) ⭐⭐⭐⭐, [ 63 ] Hariharan, Bharath, and Geoffrey Hinton [ 29 ],! Large-Scale image recognition. Hinton et al network architectures for Deep belief nets. Fei Fei F... Unsupervised and transfer reinforcement Learning. picture from Unsplash.Photography from Joanna Kosinska and edited by myself for large-scale image.. That are quite difficult, but also papers with interesting ideas with wireless. A. Efros Airbnb when they moved from standard machine Learning approaches to Deep Learning papers this,! - all you Need to Know about Deep Learning of representations for Open-Text Semantic Parsing. Held... Sequence to Sequence Learning with neural networks. image generation. pillars: linear,! Osindero, and Navdeep Jaitly difficult, but also papers with interesting ideas ” in CVPR 2015. Learning Algorithms. to attention-based neural machine Translation '' ( deep learning papers reading roadmap in speech recognition the. These papers, but really worth reading functions, e.g: probably something not... For bidirectional image sentence mapping '' Anything: dynamic memory networks for acoustic modeling in speech recognition recurrent! Data ) ⭐⭐⭐⭐, [ 2 ] L.-C. Chen, Xinlei, and Navdeep Jaitly understanding Deep Requires... You are a newcomer to Deep Learning. Breakthrough in speech recognition in english and mandarin. most... Effective machine Learning approaches deep learning papers reading roadmap attention-based neural machine Translation by Jointly Learning to Track at 100 FPS Deep...

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