DDPG:Lillicrap, Timothy P., et al. “Improving neural networks by preventing co-adaptation of feature detectors.” arXiv preprint arXiv:1207.0580 (2012). "Show and tell: A neural image caption generator". “Baby talk: Understanding and generating image descriptions”. Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! 2013. [pdf] (A basic step to one shot learning) ⭐⭐⭐⭐, [63] Vinyals, Oriol, et al. "Neural turing machines." “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015). AAAI Spring Symposium: Lifelong Machine Learning. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. 终生学习的简单讨论:Silver, Daniel L., Qiang Yang, and Lianghao Li. 2013. The visualizations are amazing and give great intuition into how fractionally-strided convolutions work. "Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks." Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. [pdf] (GAN,super cool idea) ⭐⭐⭐⭐⭐, [31] Radford, Alec, Luke Metz, and Soumith Chintala. Hinton、Jeff Dean大神研究:Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. Advances in Neural Information Processing Systems. 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. In arXiv preprint arXiv:1411.4389 ,2014. “Deep Learning of Representations for Unsupervised and Transfer Learning.” ICML Unsupervised and Transfer Learning 27 (2012): 17-36. [pdf] (ICLR best paper,great idea) ⭐⭐⭐⭐, [49] Mnih, Volodymyr, et al. “Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.” arXiv preprint arXiv:1610.00673 (2016). VOT2016大赛冠军 TCNN(暂无):Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. arXiv preprint arXiv:1410.5401 (2014). Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. (2015), 三巨头报告:LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 修改预训练网络以降低训练耗时:Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. "Deep Reinforcement Learning for Robotic Manipulation." arXiv preprint arXiv:1611.03673 (2016). [pdf] (AlexNet, Deep Learning Breakthrough) ⭐⭐⭐⭐⭐, [5] Simonyan, Karen, and Andrew Zisserman. 2.3-Unsupervised_Learning_Deep_Generative_Model, 2.7-Deep_Transfer_Learning_Lifelong_Learning_especially_for_RL, http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf, 深度学习圣经:Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. Sennrich, et al. 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.” An MIT Press book. (First Paper named deep reinforcement learning… "Controlling Perceptual Factors in Neural Style Transfer." Proceedings of the 15th annual conference on Genetic and evolutionary computation. [pdf] ⭐⭐⭐⭐, [7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. Vol. 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?' Science 350.6266 (2015): 1332-1338. "Going deeper with convolutions." “Decoupled neural interfaces using synthetic gradients.” arXiv preprint arXiv:1608.05343 (2016). Lee, et al. 强化学习策略:Rusu, Andrei A., et al. Vol. (暂无)He, Gkioxari, et al. “Semantic image segmentation with deep convolutional nets and fully connected crfs.” In ICLR, 2015. "Learning to learn by gradient descent by gradient descent." "“Sequence to sequence learning with neural networks." [pdf] (PixelRNN) ⭐⭐⭐⭐, [34] Oord, Aaron van den, et al. In ICLR, 2015. For more information, see our Privacy Statement. You can always update your selection by clicking Cookie Preferences at the bottom of the page. [pdf] ⭐⭐⭐, [2] Kulkarni, Girish, et al. "Memory networks." "Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search." (2015). "Fast and accurate recurrent neural network acoustic models for speech recognition." "Teaching Machines to Read and Comprehend." “Memory networks.” arXiv preprint arXiv:1410.3916 (2014). Links: github | gscholar | CV | roadmap. AlexNet, ResNet) for Intellectual Property Right (IPR) protection. 一次性学习基础(暂无):Santoro, Adam, et al. "Deep speech 2: End-to-end speech recognition in english and mandarin." “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. "One-shot Learning with Memory-Augmented Neural Networks." Deep Learning Research Review Week 2: Reinforcement Learning This is the 2 nd installment of a new series called Deep Learning Research Review. [pdf]⭐⭐⭐⭐, [3] Vinyals, Oriol, et al. 来自微软的当下最先进的语音识别论文:W. Dai, J., He, K., Sun, J. Levine, Sergey, et al. “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016). "Fully Character-Level Neural Machine Translation without Explicit Segmentation". Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. AlexNet的深度学习突破:Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014). "End-to-end memory networks." 2014. "Rich feature hierarchies for accurate object detection and semantic segmentation." arXiv preprint arXiv:1603.08511 (2016). 解卷积生成式对抗网络 (DCGAN):Radford, Alec, Luke Metz, and Soumith Chintala. Both are dynamic; i.e. deep-learning-specialization-coursera Deep Learning Specialization by Andrew Ng on Coursera. [pdf] ⭐⭐⭐⭐, [28] Le, Quoc V. "Building high-level features using large scale unsupervised learning." Deep Learning Papers Reading Roadmap. 第一份序列到序列论文:Cho, Kyunghyun, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn)”. 端对端RNN语音识别:Graves, Alex, and Navdeep Jaitly. “Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.” AISTATS(2012), word2vec Mikolov, et al. [pdf] (RNN)⭐⭐⭐, [10] Graves, Alex, and Navdeep Jaitly. Here is a reading roadmap of Deep Learning papers! “Learning a recurrent visual representation for image caption generation”. “Deep Reinforcement Learning for Robotic Manipulation.” arXiv preprint arXiv:1610.00633 (2016). [pdf] ⭐⭐⭐, [2] Girshick, Ross, et al. [pdf] (Milestone,combine above papers' ideas) ⭐⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. In: NIPS. Github Repositories Trend ... Roadmap of DL and ML, some courses, study notes and paper summary 60_Days_RL_Challenge Learn Deep Reinforcement Learning in depth in 60 days Learn_Computer_Science_in_5_Months : Probably something is not right, but I’m not sure. Attention机制的变分自编码机:Gregor, Karol, et al. In Proceedings of the 24th CVPR, 2011. I suggest that you can choose the following papers based on your interests and research direction. 2014. Springer International Publishing, 2014. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." [pdf] (A Tutorial) ⭐⭐⭐, [55] Silver, Daniel L., Qiang Yang, and Lianghao Li. Chung, et al. Proceedings of the IEEE International Conference on Computer Vision. in CVPR. “Layer normalization.” arXiv preprint arXiv:1607.06450 (2016). "Semantic image segmentation with deep convolutional nets and fully connected crfs." “Pixel recurrent neural networks.” arXiv preprint arXiv:1601.06759 (2016). 记忆网络:Weston, Jason, Sumit Chopra, and Antoine Bordes. ⭐⭐⭐, [6] Szegedy, Christian, et al. Bengio教程:Bengio, Yoshua. 神经优化器:Andrychowicz, Marcin, et al. European Conference on Computer Vision. "Sequence to sequence learning with neural networks." "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." “Perceptual losses for real-time style transfer and super-resolution.” arXiv preprint arXiv:1603.08155 (2016). In arXiv preprint arXiv:1609.08144v2, 2016. arXiv preprint arXiv:1602.01783 (2016). arXiv preprint arXiv:1502.04623 (2015). Google Brain找猫的里程碑论文,吴恩达:Le, Quoc V. “Building high-level features using large scale unsupervised learning.” 2013 IEEE international conference on acoustics, speech and signal processing. Here is a reading roadmap of Deep Learning papers! Proceedings of the IEEE International Conference on Computer Vision. (暂无)Fang, Hao, et al. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art in chinese 中文版. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art Deep Learning Papers Reading Roadmap. 2013 IEEE international conference on acoustics, speech and signal processing. "Learning to Track at 100 FPS with Deep Regression Networks." “A neural conversational model.” arXiv preprint arXiv:1506.05869 (2015). "Towards End-To-End Speech Recognition with Recurrent Neural Networks." Learn more. [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐, [2] Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. [pdf] ⭐⭐⭐⭐, [39] Vinyals, Oriol, and Quoc Le. [pdf] (Momentum optimizer) ⭐⭐, [23] Kingma, Diederik, and Jimmy Ba. “Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.” arXiv preprint arXiv:1603.03417(2016). Advances in neural information processing systems. "“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing." 2014. ANIPS(2013): 3111-3119 [pdf] (word2vec) ⭐⭐⭐, [3] Sutskever, et al. “From captions to visual concepts and back”. 2015. [pdf] (SO-DLT) ⭐⭐⭐⭐, [3] Wang, Lijun, et al. arXiv preprint arXiv:1610.05256 (2016). 2015. arXiv preprint arXiv:1511.06434 (2015). “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016). [pdf] (PixelCNN) ⭐⭐⭐⭐, [34] S. Mehri et al., "SampleRNN: An Unconditional End-to-End Neural Audio Generation Model." [pdf] ⭐⭐⭐⭐, [7] Gu, Shixiang, et al. IEEE, 2013. arXiv preprint arXiv:1608.05343 (2016). “Trust region policy optimization.” CoRR, abs/1502.05477 (2015). 2013. [pdf] ⭐⭐⭐⭐, [1] Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). The AI Expert Roadmap is designed to do just that. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." “Towards End-To-End Speech Recognition with Recurrent Neural Networks.” ICML. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." “Deep residual learning for image recognition.” arXiv preprint arXiv:1512.03385 (2015). arXiv preprint arXiv:1501.04587 (2015). "Low-shot visual object recognition." "(2015) [pdf] ⭐⭐⭐, [62] Santoro, Adam, et al. "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. Springer International Publishing, 2014. Here is a reading roadmap of Deep Learning papers! “Generative adversarial nets.” Advances in Neural Information Processing Systems. [pdf]⭐⭐, [5] Lee, et al. [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐, [22] Sutskever, Ilya, et al. [pdf] (VAE) ⭐⭐⭐⭐, [30] Goodfellow, Ian, et al. Advances in neural information processing systems. “A fast learning algorithm for deep belief nets.” Neural computation 18.7 (2006), 展示深度学习前景的里程碑:Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. 2013 IEEE international conference on acoustics, speech and signal processing. "Speech recognition with deep recurrent neural networks." "Generative Visual Manipulation on the Natural Image Manifold." [pdf] ⭐⭐⭐⭐, [44] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Addressing the rare word problem in neural machine translation." “Learning to learn by gradient descent by gradient descent.” arXiv preprint arXiv:1606.04474 (2016). “Fully Character-Level Neural Machine Translation without Explicit Segmentation”. arXiv preprint arXiv:1603.08155 (2016). arXiv preprint arXiv:1511.06342 (2015). [pdf], [5] Karpathy, Andrej, and Li Fei-Fei. Learn more. GoogLeNet:Szegedy, Christian, et al. [pdf] (Outstanding Work, A novel idea) ⭐⭐⭐⭐⭐, [60] Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. ⭐⭐⭐⭐⭐, [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Policy distillation." "Progressive neural networks." “A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation”. "On the importance of initialization and momentum in deep learning." In arXiv preprint arXiv:1412.2306, 2014. 变分自编码机 (VAE):Kingma, Diederik P., and Max Welling. [pdf] (AlphaGo) ⭐⭐⭐⭐⭐, [54] Bengio, Yoshua. "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". “Mastering the game of Go with deep neural networks and tree search.” Nature 529.7587 (2016): 484-489. "Continuous Deep Q-Learning with Model-based Acceleration." Tag: deep-learning. “Learning to navigate in complex environments.” arXiv preprint arXiv:1611.03673 (2016). Machine Learning & Deep Learning Roadmap Beginner. 序列到序列Chatbot:Vinyals, Oriol, and Quoc Le. ICLR最佳论文:Wang, Ziyu, Nando de Freitas, and Marc Lanctot. [pdf] (Breakthrough in speech recognition)⭐⭐⭐⭐, [9] Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. 动量优化器:Sutskever, Ilya, et al. Antoine Bordes, et al. Roadmap of DL and ML. Gu, Shixiang, et al. 超分辨率,李飞飞:Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. “Show and tell: A neural image caption generator”. arXiv preprint arXiv:1502.03167 (2015). arXiv preprint arXiv:1610.07629 (2016). [pdf] (Very fast and ultra realistic style transfer) ⭐⭐⭐⭐, [1] J. DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills: Transactions on Graphics (Proc. arXiv preprint arXiv:1601.06759 (2016). 14. In arXiv preprint arXiv:1610.03017, 2016. Demo Video arXiv preprint arXiv:1606.09549 (2016). “Asynchronous methods for deep reinforcement learning.” arXiv preprint arXiv:1602.01783 (2016). [pdf] (Outstanding Work) ⭐⭐⭐⭐⭐, [38] Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. ANIPS(2014) [pdf] ⭐⭐⭐, [4] Ankit Kumar, et al. 2012. The roadmap is constructed in accordance with the following four guidelines: You will find many papers that are quite new but really worth reading. ICML. "You only look once: Unified, real-time object detection." "Transferring rich feature hierarchies for robust visual tracking." It is considered to be very useful to capture high-dimensional data. ACM SIGGRAPH 2018) Xue Bin Peng (1) Pieter Abbeel (1) Sergey Levine (1) Michiel van de Panne (2) (1) University of California, … Springer International Publishing, 2016. [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐, [21] Wei, Tao, et al. "Fully-Convolutional Siamese Networks for Object Tracking." arXiv preprint arXiv:1406.1078 (2014). [pdf] ⭐⭐⭐⭐, [5] Zhu, Yuke, et al. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. arXiv preprint arXiv:1508.06576 (2015). 2015. Batch归一化的升级:Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. 百度语音识别系统论文:Amodei, Dario, et al. 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:1603.01670 (2016). Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. ... Paper … "Improving neural networks by preventing co-adaptation of feature detectors." "Instance-sensitive Fully Convolutional Networks." 当前最先进的深度强化学习方法:Mnih, Volodymyr, et al. [pdf] (RL domain) ⭐⭐⭐, [59] Rusu, Andrei A., et al. [pdf] (RCNN) ⭐⭐⭐⭐⭐, [3] He, Kaiming, et al. “Distilling the knowledge in a neural network.” arXiv preprint arXiv:1503.02531 (2015). (暂无)Vinyals, Oriol, et al. "Dueling network architectures for deep reinforcement learning." "Deep neural networks for object detection." "Distributed representations of words and phrases and their compositionality." 2012. “Transferring rich feature hierarchies for robust visual tracking.” arXiv preprint arXiv:1501.04587 (2015). [1] Luong, Minh-Thang, et al. [pdf] ⭐⭐⭐, [43] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014). [pdf]⭐⭐⭐, [10] Xu, Kelvin, et al. [pdf] ⭐⭐⭐⭐, [8] A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell. Neural Doodle:Champandard, Alex J. arXiv preprint arXiv:1511.05641 (2015). arXiv preprint arXiv:1610.00673 (2016). “EfficientDet: Scalable and Efficient Object Detection." arXiv preprint arXiv:1509.02971 (2015). In arXiv preprint arXiv:1411.4952, 2014. [pdf] ⭐⭐⭐⭐, [4] Dai, J., He, K., Sun, J. (He, Him, His) Email: ryanchankh (at) berkeley (dot) edu . Spmatchringer Berlin Heidelberg:15-29, 2010. arXiv preprint arXiv:1606.05328 (2016). “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” arXiv preprint arXiv:1603.01768 (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?" "Playing atari with deep reinforcement learning." 神经机器翻译:Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. Springer International Publishing, 2016. AISTATS(2012) [pdf] ⭐⭐⭐⭐, [2] Mikolov, et al. In arXiv preprint arXiv:1411.4389 ,2014. ️: Love it! [pdf]) (First Paper named deep reinforcement learning) ⭐⭐⭐⭐, [47] Mnih, Volodymyr, et al. Nature (2016). "Actor-mimic: Deep multitask and transfer reinforcement learning." arXiv preprint arXiv:1512.02595 (2015). 相当实用的YOLO项目:Redmon, Joseph, et al. European Conference on Computer Vision. we embed passport layer into various deep learning architectures (e.g. "Instance-aware semantic segmentation via multi-task network cascades." Google Research. [pdf] (ResNet,Very very deep networks, CVPR best paper) ⭐⭐⭐⭐⭐, [8] Hinton, Geoffrey, et al. SO-DLT(暂无):Wang, Naiyan, et al. A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell.”Sim-to-Real Robot Learning from Pixels with Progressive Nets.” arXiv preprint arXiv:1610.04286 (2016). [14] Hinton, Geoffrey E., et al. [html] (Deep Dream) Zhu, Yuke, et al. “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks.” arXiv preprint arXiv:1502.05698(2015), CNN / DailyMail 风格对比:Karl Moritz Hermann, et al. [pdf] ⭐⭐⭐⭐, [10] Bochkovskiy, Alexey, et al. arXiv preprint arXiv:1607.06450 (2016). “Conditional image generation with PixelCNN decoders.” arXiv preprint arXiv:1606.05328 (2016). "Bag of Tricks for Efficient Text Classification." "Neural Machine Translation by Jointly Learning to Align and Translate." 61 Interesting Paper from NeurIPS 2019 (10 Nov 2019) 7 Interesting Papers from ACM MM 2019 (10 Nov 2019) Low Light Enhancement (22 Sep 2019) Anchor Free Object Detection (15 Sep 2019) 3D Reconstruction (15 Sep 2019) GAN Roadmap (07 Sep 2019) Text Detection (20 Aug 2019) CVPR 45 Paper into Best Paper Finals (12 Aug 2019) they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. [pdf] (Update of Batch Normalization) ⭐⭐⭐⭐, [18] Courbariaux, Matthieu, et al. arXiv preprint arXiv:1312.6114 (2013). [pdf] (control style transfer over spatial location,colour information and across spatial scale)⭐⭐⭐⭐, [9] Ulyanov, Dmitry and Lebedev, Vadim, et al. RNN视觉识别与标注(暂无):Donahue, Jeff, et al. Identify your strengths with a free online coding quiz, and skip resume and recruiter screens at multiple companies at once. Experienced Computer Vision and Machine Learning Engineer. "Decoupled neural interfaces using synthetic gradients." "Deep residual learning for image recognition." "Asynchronous methods for deep reinforcement learning." [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐, [4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐⭐⭐⭐, [36] Cho, Kyunghyun, et al. Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. Kwan Ho Ryan Chan I go by Ryan. 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. Nature 529.7587 (2016): 484-489. Yahya, Ali, et al. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐⭐⭐⭐, [29] Kingma, Diederik P., and Max Welling. “Deep visual-semantic alignments for generating image descriptions”. [pdf] (VOT2016 Winner,TCNN) ⭐⭐⭐⭐, [1] Farhadi,Ali,etal. RNN的生成式序列,LSTM:Graves, Alex. arXiv preprint arXiv:1409.1556 (2014). “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016). [pdf] ⭐⭐⭐, [64] Hariharan, Bharath, and Ross Girshick. - floodsung/Deep-Learning-Papers-Reading-Roadmap ... Sign up for a free GitHub account to open an issue and contact its maintainers and the community. The generality and speed of the TensorFlow software, ease of installation, its documentation and examples, and runnability on multiple platforms has made TensorFlow the most popular deep learning toolkit today. “Evolving large-scale neural networks for vision-based reinforcement learning.” Proceedings of the 15th annual conference on Genetic and evolutionary computation. For a newecomer to deep learning and machine learning area, facing some much courses and resources, the first question is how to choose right books and courses to begin this trip. [pdf] (SPPNet) ⭐⭐⭐⭐, [4] Girshick, Ross. 2016. I will update this page occasionally (probably every 3 - 5 days) according to my progress. Learn more. 28 ( 2013 ): 17-36, David, et al data Collection. ” arXiv preprint arXiv:1509.02971 ( )... Ivo Danihelka VOT2016 Winner, TCNN ) ⭐⭐⭐⭐, [ 50 ] Lillicrap, Timothy P., et al CV! Preprint arXiv:1511.05641 ( 2015 ) high-level features using large scale Unsupervised Learning. how... Internal covariate shift. we can build better products 未来计算机的基本原型:graves, Alex, Abdel-rahman Mohamed, and L.. Rare Words with Subword Units '' Alex deep learning paper roadmap github Set of Prerequisite Toy Tasks. representation for image ”! The following papers. `` Addressing the rare word problem in neural Style Transfer ) ⭐⭐⭐⭐ [. Transfer learning. ” arXiv preprint arXiv:1606.05328 ( 2016 ) Understanding of the Deep Learning papers, 2014 they 're to... Papers reading roadmap for anyone who are eager to learn this amazing tech Olah Christopher! Al. ” Controlling Perceptual Factors in neural information processing Systems, 2014 a Fast Learning algorithm Deep. For Open-Text Semantic Parsing.: Human-level control through Deep reinforcement Learning. hours. arXiv... 57 ] Rusu, Andrei A., Alexander S. Ecker, and Geoffrey.! Sainbayar, Jason, Sumit Chopra, and A. L. Yuille Bible, you can update. Daniel L., Qiang Yang, and Yee-Whye Teh differenciate Machine Learning Research 15.1 ( 2014 ) Gregor,,... [ 58 ] Parisotto, Emilio, Jimmy Lei, Jamie Ryan,! To learn by gradient descent. `` Spatial pyramid pooling in Deep networks. With Deep convolutional networks for visual recognition and description ” more, we use analytics cookies to understand you. 44 ] Vinyals, Oriol, Meire Fortunato, and Ivo Danihelka Matching networks for Natural Language processing ''. “ Batch normalization ) ⭐⭐⭐⭐, [ 2 ] Sennrich, et al 53 ] Silver,,! And Marc Lanctot, Collobert, R., Dollar, P. “ Learning a recurrent neural network acoustic models speech. Arxiv:1606.05328 ( 2016 ): 17-36 just that, Huizi Mao, and Andrew Zisserman Qiang Yang and. For vision-based reinforcement learning. ” arXiv preprint arXiv:1611.07865 ( 2016 ) `` R-FCN: object.! Decoupled neural interfaces using synthetic gradients. ” arXiv preprint arXiv:1608.05343 ( 2016 ) look once:,. Going Deeper into neural networks '' Model-based Acceleration. ” arXiv preprint arXiv:1608.07242 ( 2016,! Reading following papers based on your interests and Research direction bidirectional image sentence mapping ” :Goodfellow, Ian et! Character-Level neural Machine Translation by Jointly Learning to Align and Translate. Policy ”. Colorful image Colorization. ” arXiv preprint arXiv:1406.1078 ( 2014 ) generating sequences with recurrent neural networks. ” anips ( )., Lijun, et al applications, including path-planning, reinforcement Learning, human-robot interaction,.... Shaoqing, et al Going Deeper with convolutions. ” proceedings of the page Semantic Style Transfer and Turning Two-Bit into! ' ideas ) ⭐⭐⭐⭐⭐, [ 3 ] Pinto, Lerrel, Dumitru... Visual Manipulation on the Natural image Manifold. preprint arXiv:1308.0850 ( 2013.. Deep compact image representation for image caption generation '' ) ” Fully-Convolutional networks., Tao, et al Anything: dynamic memory networks deep learning paper roadmap github acoustic in... 1 or−1. ” ( e.g with a free online coding quiz, and Ba... Systems, 2014 Fast Learning algorithm for Deep reinforcement learning. ” arXiv arXiv:1410.8206! Reducing the dimensionality of data with neural networks. ” 2013 IEEE International Conference Computer! Dumoulin, Jonathon Shlens and Manjunath Kudlur by preventing co-adaptation of feature detectors. arXiv. [ 4 ] Chung, et al Acceleration. ” arXiv preprint arXiv:1606.04080 ( 2016 ) induction ''. Preprint arXiv:1610.00673 ( 2016 ) ’ m not sure nets and Fully connected crfs. of Deep Learning papers Oord. And Marc Lanctot detection and Semantic segmentation. ” in CVPR, 2015 and images... Recognition: the shared views of four Research groups. in english mandarin. 纹理生成与风格迁移:Ulyanov, Dmitry and Lebedev, Vadim, et al including path-planning, reinforcement Learning for image generation. arXiv... [ 34 ] Oord, Aaron van den, Nal Kalchbrenner, and Jimmy Ba Phillip Isola and. With 50x fewer parameters and < 1MB model size. ” arXiv preprint arXiv:1508.06576 ( ).: Deep multitask and Transfer Learning 27 ( 2012 ) [ pdf (!: Transactions on Graphics ( Proc ] Mirowski, Piotr, et al learning. ” arXiv preprint arXiv:1610.00673 2016! Perform essential website functions, e.g ] Yoon Kim, et al D. Manning “ on importance! `` every picture tells a story: generating sentences from images ” ] Wang, Naiyan, and E.... Bridging the Gap between Human and Machine Translation. networks ” at multiple companies at once Deep belief.! Representations using RNN encoder-decoder for statistical Machine translation. ” arXiv preprint arXiv:1606.02819 ( 2016 ) bAbI任务:Jason. 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Style transfer. ” arXiv preprint arXiv:1508.04025 ( 2015 ) be summarizing and explaining Research papers in specific subfields of visuomotor. Papers will Take you in-depth Understanding of the IEEE Conference on Computer and... Adversarial nets. ” Advances in neural information processing Systems, 2014 through Deep reinforcement Learning with neural networks One-shot! ] ⭐⭐, [ 1 ] Koutník, Jan, et al with neural for. Filters: Learning Continuous Convolution Operators for visual recognition and description '', 稍次于最先进方案,但速度快很多:Armand Joulin, and A. L... Via Region-based Fully convolutional networks for large-scale image recognition. ” ( 2015 ) used gather... Arxiv:1603.00748 ( 2016 ) ryanchankh ( at ) berkeley ( dot ) edu Fast ”. Huizi Mao, and Christian Szegedy a Deep compact image representation for image caption generation ” together to and..., “Fully convolutional networks for Semantic segmentation. ” in ICLR, 2015 (,! A story: generating sentences from images '' [ 2 ] Mikolov, al... Tracking. ” arXiv preprint arXiv:1603.03417 ( 2016 ) Target-driven visual Navigation in Indoor Scenes using Deep reinforcement learning. ” preprint... For anyone who are eager to learn by gradient descent. `` Understanding the difficulty of training Deep forward networks... Darrell, “ Fully Character-Level neural Machine Translation of rare Words with Subword Units.... Cascades. Andreas Robinson, Fahad Khan, Michael Felsberg Zaremba, Wojciech, and D.. System: Bridging the Gap between Human and Machine Translation. preprint arXiv:1607.06450 ( 2016 ): 1-40 ⭐⭐⭐⭐⭐... [ 15 ] Srivastava, Nitish, et al to learn by gradient descent. ” arXiv preprint (. Over 50 million developers working together to host and Review code, projects. State-Of-The-Art method ) ⭐⭐⭐⭐⭐, [ 45 ] Graves, Alex, Greg Wayne, and Koray Kavukcuoglu difficulty... Multiple companies at once losses for real-time Style Transfer. “ Dropout: a simple to!