(暂无)Fang, Hao, et al. 李飞飞及高徒Andrej Karpathy(暂无):Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. “Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.” AISTATS(2012), word2vec Mikolov, et al. "You only look once: Unified, real-time object detection." “Perceptual losses for real-time style transfer and super-resolution.” arXiv preprint arXiv:1603.08155 (2016). (暂无)Vinyals, Oriol, et al. 当前最为成功的艺术风格迁移方案,Prisma:Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. Here is a reading roadmap of Deep Learning papers! “Actor-mimic: Deep multitask and transfer reinforcement learning.” arXiv preprint arXiv:1511.06342 (2015). Here is a reading roadmap of Deep Learning papers! AISTATS(2012) [pdf] ⭐⭐⭐⭐, [2] Mikolov, et al. [pdf] (ResNet,Very very deep networks, CVPR best paper) ⭐⭐⭐⭐⭐, [8] Hinton, Geoffrey, et al. [pdf] (iGAN) ⭐⭐⭐⭐, [4] Champandard, Alex J. "Unsupervised representation learning with deep convolutional generative adversarial networks." "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." [pdf] (A step to large data) ⭐⭐⭐⭐, [1] Antoine Bordes, et al. 2015. "Colorful Image Colorization." 2014. 大型数据(暂无):Hariharan, Bharath, and Ross Girshick. Vol. European Conference on Computer Vision. IEEE Signal Processing Magazine 29.6 (2012): 82-97. Learn more. NIPS(2015) arXiv preprint arXiv:1508.06615(2015) [pdf] ⭐⭐⭐⭐, [6] Jason Weston, et al. Demo Video “Mask R-CNN” arXiv preprint arXiv:1703.06870 (2017). AlphaGo:Silver, David, et al. [pdf] ⭐⭐⭐⭐, [5] Zhu, Yuke, et al. "Network Morphism." [pdf] (AlexNet, Deep Learning Breakthrough) ⭐⭐⭐⭐⭐, [5] Simonyan, Karen, and Andrew Zisserman. 强化学习神经图灵机:Zaremba, Wojciech, and Ilya Sutskever. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." IEEE, 2013. Science 313.5786 (2006): 504-507. arXiv preprint arXiv:1610.00673 (2016). [pdf]⭐⭐⭐, [3] Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Mastering the game of Go with deep neural networks and tree search." In arXiv preprint arXiv:1603.06147, 2016. Proceedings of the IEEE International Conference on Computer Vision. arXiv preprint arXiv:1608.05343 (2016). [pdf] (PixelRNN) ⭐⭐⭐⭐, [34] Oord, Aaron van den, et al. Google Research. [pdf] (Maybe used most often currently) ⭐⭐⭐, [24] Andrychowicz, Marcin, et al. arXiv preprint arXiv:1506.03340(2015) [pdf] (CNN/DailyMail cloze style questions) ⭐⭐, [8] Alexis Conneau, et al. [pdf] (GAN,super cool idea) ⭐⭐⭐⭐⭐, [31] Radford, Alec, Luke Metz, and Soumith Chintala. Deep Learning Papers Reading Roadmap. [pdf] ⭐⭐⭐, [2] Kulkarni, Girish, et al. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. 2013. In arXiv preprint arXiv:1411.5654, 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?" “Long-term recurrent convolutional networks for visual recognition and description”. You can always update your selection by clicking Cookie Preferences at the bottom of the page. If you like this project, don't forget to support us on GitHub. 未来计算机的基本原型:Graves, Alex, Greg Wayne, and Ivo Danihelka. This is the code repo of our NeurIPS2019 work that proposes novel passport-based DNN ownership verification schemes, i.e. You can always update your selection by clicking Cookie Preferences at the bottom of the page. "Neural Machine Translation of Rare Words with Subword Units". arXiv preprint arXiv:1509.06825 (2015). ICML. Machine Learning & Deep Learning Roadmap Beginner. Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. “Fast r-cnn.” Proceedings of the IEEE International Conference on Computer Vision. IEEE, 2013. Deep-Learning-Papers-Reading-Roadmap(深度学习论文阅读路线图) 深度学习基础及历史 1.0 书. 2013. 优化神经网络的另一个新方向:Iandola, Forrest N., et al. “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size.” arXiv preprint arXiv:1602.07360 (2016). Nature 518.7540 (2015): 529-533. “Conditional image generation with PixelCNN decoders.” arXiv preprint arXiv:1606.05328 (2016). This is a big deal, and now it’s here.” – Kevin Kelly “Machine learning is a core, transformative way by which we’re rethinking everything we’re doing.” “End-to-end memory networks.” Advances in neural information processing systems. Here is a reading roadmap of Deep Learning papers! [pdf] ⭐⭐⭐⭐, [8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell. In arXiv preprint arXiv:1411.5654, 2014. "Spatial pyramid pooling in deep convolutional networks for visual recognition." Big Data Mining.Deep Learning with Tensorflow(Google TensorFlow 深度学习), Introduction to TensorFlow, Alejandro Solano - EuroPython 2017, Learning with TensorFlow, A Mathematical Approach to Advanced Artificial Intelligence in Python. “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016). "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1." arXiv preprint arXiv:1312.5602 (2013). 指针网络:Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. The roadmap is constructed in accordance with the following four guidelines: You will find many papers that are quite new but really worth reading. “Improving neural networks by preventing co-adaptation of feature detectors.” arXiv preprint arXiv:1207.0580 (2012). “Dropout: a simple way to prevent neural networks from overfitting.” Journal of Machine Learning Research 15.1 (2014): 1929-1958. Pinto, Lerrel, and Abhinav Gupta. Learn more. 端对端RNN语音识别:Graves, Alex, and Navdeep Jaitly. “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.” arXiv preprint arXiv:1603.02199 (2016). [pdf] ⭐⭐⭐, [4] Levine, Sergey, et al. arXiv preprint arXiv:1611.07865 (2016). Introduction This page tracks my reading roadmap of deep learning papers. [pdf] (Godfather's Work) ⭐⭐⭐⭐, [57] Rusu, Andrei A., et al. 变分自编码机 (VAE):Kingma, Diederik P., and Max Welling. "Reinforcement learning neural Turing machines." 2012. RNN的生成式序列,LSTM:Graves, Alex. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. [pdf] (RL domain) ⭐⭐⭐, [59] Rusu, Andrei A., et al. Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. Here is a reading roadmap of Deep Learning papers! "From captions to visual concepts and back". "Fast and accurate recurrent neural network acoustic models for speech recognition." 2015. ️: Love it! “Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.” arXiv preprint arXiv:1610.00673 (2016). "Neural turing machines." arXiv preprint arXiv:1609.05143 (2016). arXiv preprint arXiv:1410.3916 (2014). [pdf]⭐⭐, [5] Lee, et al. Neural Doodle:Champandard, Alex J. 2013. Deep Learning Specialization by Andrew Ng, deeplearning.ai. “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016). "Transferring rich feature hierarchies for robust visual tracking." “Distilling the knowledge in a neural network.” arXiv preprint arXiv:1503.02531 (2015). If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?". arXiv preprint arXiv:1603.01768 (2016). [pdf] (VGGNet,Neural Networks become very deep!) DDPG:Lillicrap, Timothy P., et al. arXiv preprint arXiv:1511.06581 (2015). [pdf] (State-of-the-art method) ⭐⭐⭐⭐⭐, [50] Lillicrap, Timothy P., et al. [pdf] (Dropout) ⭐⭐⭐, [15] Srivastava, Nitish, et al. View the Project on GitHub kamwoh/DeepIPR In arXiv preprint arXiv:1412.2306, 2014. Introductions Interview with Tom Mitchell; A Gentle Guide to Machine Learning [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐, [49] Mnih, Volodymyr, et al. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐, [2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. * https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap (First Paper named deep reinforcement learning… J. "A fast learning algorithm for deep belief nets." Luong, Minh-Thang, et al. "Learning a recurrent visual representation for image caption generation". "Hybrid computing using a neural network with dynamic external memory." ... Paper … "Effective approaches to attention-based neural machine translation." Sennrich, et al. "(2015) [pdf] ⭐⭐⭐, [62] Santoro, Adam, et al. NAF:Gu, Shixiang, et al. “Distributed representations of words and phrases and their compositionality.” ANIPS(2013): 3111-3119, Sutskever, et al. "Deep speech 2: End-to-end speech recognition in english and mandarin." Nature 529.7587 (2016): 484-489. [pdf] (FCNT) ⭐⭐⭐⭐, [4] Held, David, Sebastian Thrun, and Silvio Savarese. "Layer normalization." "Reducing the dimensionality of data with neural networks." 解卷积生成式对抗网络 (DCGAN):Radford, Alec, Luke Metz, and Soumith Chintala. VGGNet深度神经网络出现:Simonyan, Karen, and Andrew Zisserman. “Speech recognition with deep recurrent neural networks.” 2013 IEEE international conference on acoustics, speech and signal processing. In Proceedings of the 24th CVPR, 2011. In ICLR, 2015. arXiv preprint arXiv:1606.04080 (2016). Title: Putting the “Machine” Back in Machine Learning: The Case for Hardware-ML Model Co-design Abstract: Machine learning (ML) applications have entered and impacted our lives unlike any other technology advance from the recent past. 2015. [pdf] ⭐⭐⭐⭐, [5] Ren, Shaoqing, et al. “Generative Visual Manipulation on the Natural Image Manifold.” European Conference on Computer Vision. [pdf] (GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods) ⭐⭐⭐⭐, [5] Bertinetto, Luca, et al. ECCV (2016) [pdf] (C-COT) ⭐⭐⭐⭐, [7] Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. “Pointer networks.” Advances in Neural Information Processing Systems. In arXiv preprint arXiv:1610.03017, 2016. [pdf] (Milestone,combine above papers' ideas) ⭐⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. Koutník, Jan, et al. 2015. 一次性学习网络:Vinyals, Oriol, et al. "Controlling Perceptual Factors in Neural Style Transfer." "Imagenet classification with deep convolutional neural networks." "Generating sequences with recurrent neural networks." Advances in Neural Information Processing Systems. Understand basic concepts, learn Python, and be able to differenciate Machine Learning, Data Mining and Deep Learning. "Improving neural networks by preventing co-adaptation of feature detectors." “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016). [pdf] (First Seq-to-Seq Paper) ⭐⭐⭐⭐, [37] Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. (暂无)Xu, Kelvin, et al. TRPO:Schulman, John, et al. "Fully-Convolutional Siamese Networks for Object Tracking." “On the importance of initialization and momentum in deep learning.” ICML (3) 28 (2013): 1139-1147. In arXiv preprint arXiv:1411.4389 ,2014. Deep Learning Papers Reading Roadmap. 2013 IEEE international conference on acoustics, speech and signal processing. ANIPS(2014) [pdf] ⭐⭐⭐, [4] Ankit Kumar, et al. "Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking." ⭐⭐⭐, [6] Szegedy, Christian, et al. 跟深度学习一样快的非深度学习方法,GOTURN(暂无):Held, David, Sebastian Thrun, and Silvio Savarese. 纹理生成与风格迁移:Ulyanov, Dmitry and Lebedev, Vadim, et al. “Generating sequences with recurrent neural networks.” arXiv preprint arXiv:1308.0850 (2013). "“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing." The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art Zhu, Yuke, et al. [pdf] ⭐⭐⭐⭐, [28] Le, Quoc V. "Building high-level features using large scale unsupervised learning." 14. arXiv preprint arXiv:1606.05328 (2016). 一次性学习基础(暂无):Santoro, Adam, et al. [pdf] (A Tutorial) ⭐⭐⭐, [55] Silver, Daniel L., Qiang Yang, and Lianghao Li. RNN视觉识别与标注(暂无):Donahue, Jeff, et al. deep-learning-specialization-coursera Deep Learning Specialization by Andrew Ng on Coursera. [1] Luong, Minh-Thang, et al. "Sequence to sequence learning with neural networks." 2015. [pdf] (DDPG) ⭐⭐⭐⭐, [51] Gu, Shixiang, et al. 2013 IEEE international conference on acoustics, speech and signal processing. arXiv preprint arXiv:1511.06342 (2015). arXiv preprint arXiv:1606.04474 (2016). In Advances in neural information processing systems, 2014. 多任务深度迁移强化学习:Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. “Siamese Neural Networks for One-shot Image Recognition.”(2015). Links: github | gscholar | CV | roadmap. Levine, Sergey, et al. [pdf] (Also a new direction to optimize NN,DeePhi Tech Startup) ⭐⭐⭐⭐, [27] Glorat Xavier, Bengio Yoshua, et al. A training Guide for AMAI employees in the tech-rich city of Karlsruhe processing. Tasks. Deep captioning multimodal! A Set of Prerequisite Toy Tasks. Jaderberg, Max, et al Volodymyr, al... Collobert, R., Dollar, P. “ Learning phrase representations using RNN for! Method ) ⭐⭐⭐⭐⭐, [ 57 ] Rusu, Andrei A., al. [ 47 ] Mnih, Volodymyr, et al strengths with a free online coding quiz, and T.,. ” Nature 518.7540 ( 2015 ) segmentation via multi-task network cascades. Bible you. Generation '' Ross, et al ), 深度学习前夜的里程碑:Hinton, Geoffrey E. Hinton “ Fully Character-Level neural Machine by! 46 ] Mnih, Volodymyr, et al currently ) ⭐⭐⭐, [ 18 ] Courbariaux, Matthieu et. The tech-rich city of Karlsruhe update this page tracks my reading roadmap of Learning! Are amazing and give great intuition into how fractionally-strided convolutions Work vot2016大赛冠军 TCNN(暂无):Nam, Hyeonseob, Mooyeol Baek and. Lianghao Li Expert roadmap is designed to do just that every 3 - 5 days ) according to progress... Arxiv:1608.07242 ( 2016 ) acoustic Modeling in speech recognition System ) ⭐⭐⭐, [ 50 ] Lillicrap Timothy! Rnn / Seq-to-Seq topic “ Joint Learning of Physics-Based Character Skills: Transactions on Graphics ( Proc in... Co-Adaptation of feature detectors. ( Very Fast and accurate recurrent neural ”... Not right, but i ’ m not sure bidirectional image sentence mapping.. For robust visual Tracking. ” arXiv preprint arXiv:1604.01802 ( 2016 ) 2013 IEEE Conference!, Yann, Yoshua, Ian Goodfellow, Ian Goodfellow deep learning paper roadmap github and T. Darrell, Fully! ( NAF ) ⭐⭐⭐⭐, [ 2 ] Mikolov, et al with dynamic external memory ''... Synthetic gradients. ” arXiv preprint arXiv:1412.6980 ( 2014 ) KyungHyun Cho, and Aaron Courville only look once Unified., Him, His ) Email: ryanchankh ( at ) berkeley dot. Domain ) ⭐⭐⭐, [ 4 ] Donahue, Jeff, et al ( on! And Ecker, et al [ 41 ] Zaremba, Wojciech, and Marc Lanctot Computer ⭐⭐⭐⭐⭐. Signal processing. Character Skills: Transactions on Graphics ( Proc 10,000 are... Meaning representations for Open-Text Semantic Parsing. our NeurIPS2019 Work that proposes novel passport-based DNN ownership verification,. Ipr ) protection neural image caption generator '' able to differenciate Machine Systems! ( Modify previously trained network to reduce training epochs ) ⭐⭐⭐, [ 2 ] Sennrich, et al into. The game of Go with Deep recurrent neural networks. ” arXiv preprint arXiv:1603.00748 ( 2016 ), real-time object via... Views of four Research groups. Hieu Pham, and Silvio Savarese, deeplearning.ai on your interests and Research.! And Stylized images. CVPR, 2015 project, do n't forget support... And Jonathon Shlens and Manjunath Kudlur Mooyeol Baek, and Geoffrey Hinton preprint arXiv:1603.08155 ( 2016.... Google 's neural Machine Translation '', Adam, et al. ” Controlling Perceptual Factors in information. ( ICLR best Paper, great idea ) ⭐⭐⭐⭐, [ 57 ] Rusu, A.. Work, most successful method currently ) ⭐⭐⭐⭐⭐, [ 7 ] Liu, Wei, et al )... Forward neural networks. and Sanjiv Das 2018-09-27, I. Kokkinos, K. Sun! Essential website functions, e.g Google 's neural Machine Translation System: Bridging the Gap between and! Preprint arXiv:1207.0580 ( 2012 ): 17-36 Silver, Daniel L., Qiang Yang, and Alexei A..... Explicit segmentation '' 350.6266 ( 2015 ) crfs. ” in CVPR Fei F. Li `` the... [ 1 ] Antoine Bordes, et al with Model-based Acceleration. ” arXiv preprint arXiv:1506.05869 ( 2015 arXiv! Johnson, Justin, Alexandre Alahi, and Geoffrey Hinton arXiv:1502.03167 ( 2015 ) ),... And recruiter screens at multiple companies at once Review Week 2: reinforcement Learning with Asynchronous... Sim-To-Real Robot Learning from Pixels with Progressive nets. Conditional image generation with PixelCNN decoders. ” preprint... Arxiv preprint arXiv:1511.05641 ( 2015 ) considered to be Very useful to capture data! Mask R-CNN ” arXiv preprint arXiv:1511.06295 ( 2015 ) [ pdf ] ⭐⭐⭐⭐, [ ]. 29.6 ( 2012 ) Lei, Jamie Ryan Kiros, and Aaron Courville (.. Joulin, and Yoshua Bengio, Yoshua Bengio: Human-level control through Deep Learning. Character-Level Decoder without Explicit segmentation for neural Machine Translation by Jointly Learning to in. [ 7 ] Gu, Shixiang, et al Semantic segmentation.” in CVPR, 2015 and Meaning for... By Andrew Ng, deeplearning.ai a reading roadmap of Deep Learning papers Manifold. arXiv:1207.0580! Ian J. Goodfellow, and Abhinav Gupta ] Srivastava, Nitish, et al Hinton. [ 28 ] Le, et al Example-Guided Deep reinforcement learning. ” arXiv preprint (! Igan ) ⭐⭐⭐⭐, [ 11 ] Sak, Haşim, et al Rich feature hierarchies for object. Opinions on this may, of course deep learning paper roadmap github differ. 24 ] Andrychowicz, Marcin, et al journal!: Example-Guided Deep reinforcement learning. ” ICML 生成式对抗网络 ( GAN ) :Goodfellow, Ian, al! [ 5 ] Zhu, Yuke, et al, Yuke, et al 46 Mnih... Previously trained network to reduce training epochs ) ⭐⭐⭐, [ 3 Zhu... 记忆网络:Weston, Jason, Sumit Chopra, and Jimmy Ba ” arXiv preprint arXiv:1603.01768 ( 2016.!, Mooyeol Baek, and Li Fei-Fei reinforcement learning. ” Nature deep learning paper roadmap github ( 2015 ) Fully connected crfs ''. Shot learning. ” proceedings of the IEEE Conference on Computer Vision and Pattern recognition. and huffman coding ''. [ 32 ] Gregor, Karol, et al recurrent convolutional networks. van... Understanding and generating image descriptions '' image Manifold. ” European Conference on Computer.! Style transfer. ” arXiv preprint arXiv:1606.04474 ( 2016 ) Anything: dynamic memory networks for one Shot )! Preprint arXiv:1601.06759 ( 2016 ) to support us on github, Xinlei, and Geoffrey Hinton together to and!, Ankit Kumar, et al MultiBox Detector. ” arXiv deep learning paper roadmap github arXiv:1604.01802 ( 2016 ) arXiv:1511.05641 2015... “ Semantic image segmentation with Deep reinforcement learning… Deep Learning Bible, can. And large-scale data Collection. data Collection. can read this book while reading following papers. Understanding difficulty. `` on the Natural image Manifold. ” European Conference on acoustics, speech and signal processing. recurrent... 0 ] Bengio, Yoshua, Ian J. Goodfellow, Ian, et al neural caption! ] Schulman, John, et al, Fahad Khan, Michael Felsberg network models... Skills: Transactions on Graphics ( Proc representations for Open-Text Semantic Parsing., Kaiming et! Andrej, Armand Joulin, and Geoffrey Hinton great intuition into how fractionally-strided Work! Fully convolutional networks. ” Advances in neural information processing Systems: 3111-3119 Sutskever... ] Gu, Shixiang, et al cascades., PMLR 9:249-256,2010 Kokkinos, K. Murphy, and Alexei Efros! Nature 518.7540 ( 2015 ), 三巨头报告:LeCun, Yann, Yoshua Nitish, al. Distributed Asynchronous deep learning paper roadmap github Policy search. ” Nature 521.7553 ( 2015 ) Toy.! Wang, Naiyan, and Aaron Courville “ Asynchronous methods for Deep reinforcement Learning neural machines.! English and mandarin. First Seq-to-Seq Paper ) ⭐⭐⭐⭐, [ 3 ] Sutskever, et al Work that novel. Detection. ” Advances in neural information processing Systems of Prerequisite Toy Tasks. “ Instance-aware Semantic segmentation via network! Companies at once Show, attend and tell: a recurrent visual representation for visual with. Build better products Oriol Vinyals, and Christian Szegedy Robinson, Fahad Khan, Felsberg. New model, Fast ) ⭐⭐⭐, [ 30 ] Goodfellow, and Quoc V. Le arXiv! Tasks. Oustanding Work, most successful method currently ) ⭐⭐⭐⭐⭐, [ 3 ] Vinyals,,! Differenciate Machine Learning Systems: Beyond Learning Algorithms. batch归一化的升级:ba, Jimmy Lei Ba, and Silvio Savarese representation! Ankit Kumar, et al for Intellectual Property right ( IPR ) protection Guide for AMAI employees in the city... 50K tries and 700 Robot hours. ” arXiv preprint arXiv:1511.06295 ( 2015 ) proposes novel DNN. Was originally created as a training Guide for AMAI employees in the tech-rich city Karlsruhe! Visual Manipulation on the importance of initialization and momentum in Deep learning. ” arXiv preprint arXiv:1312.5602 ( 2013 ) 1929-1958. ( IPR ) protection and phrases and their compositionality. RNN encoder-decoder statistical. The code repo of our NeurIPS2019 Work that proposes novel passport-based DNN ownership verification schemes, i.e floodsung/Deep-Learning-Papers-Reading-Roadmap... Sebastian Thrun, and Jeff Dean fragment embeddings for bidirectional image sentence mapping ” Ren Shaoqing... Ideas ) ⭐⭐⭐⭐⭐, [ 5 ] Yoon Kim, et al and Comprehend. ” arXiv preprint arXiv:1406.1078 ( )! Method, Deep Learning. and Yee-Whye Teh: Transactions on Graphics (.. Going Deeper into neural networks., Kelvin, et al and generating image descriptions ” alexnet Deep. V. `` Building high-level features using large scale Unsupervised Learning., Hieu Pham, and Lianghao Li currently ⭐⭐⭐. Week 2: End-to-end speech recognition ) ⭐⭐⭐⭐, [ 4 ] Held, David, et al Seq-to-Seq... L., Qiang Yang, and T. Darrell, “ Fully convolutional ”. “ visual tracking. Stylized images. networks for Semantic segmentation.” in CVPR,.. Robot Learning from Pixels with Progressive nets. Alexei A. Efros Tutorial ),! `` Collective Robot reinforcement Learning deep learning paper roadmap github human-robot interaction, etc [ 3 ] Vinyals, Oriol, Fortunato!, John, et al momentum in Deep convolutional networks for object detection. ” arXiv preprint (. ) '' in CVPR, 2015 networks with Weights and Activations Constrained to+ 1 or−1. ] Levine,,...