“Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours.” arXiv preprint arXiv:1509.06825 (2015). ResNet极深度神经网络,CVPR最佳论文:He, Kaiming, et al. “You only look once: Unified, real-time object detection.” arXiv preprint arXiv:1506.02640 (2015). (2015). "Deep captioning with multimodal recurrent neural networks (m-rnn)". “Net2net: Accelerating learning via knowledge transfer.” arXiv preprint arXiv:1511.05641 (2015). "Deep residual learning for image recognition." Vincent Dumoulin and Francesco Visin’s paper “A guide to convolution arithmetic for deep learning” and conv_arithmetic project is a very well-written introduction to convolution arithmetic in deep learning. 可能是当前使用最多的随机优化:Kingma, Diederik, and Jimmy Ba. arXiv preprint arXiv:1605.06065 (2016). L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. "Deep visual-semantic alignments for generating image descriptions". Introductions Interview with Tom Mitchell; A Gentle Guide to Machine Learning 2015. "Low-shot visual object recognition." 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. arXiv preprint arXiv:1409.1556 (2014). “Towards End-To-End Speech Recognition with Recurrent Neural Networks.” ICML. The project was originally created as a training guide for AMAI employees in the tech-rich city of Karlsruhe. "Fully Character-Level Neural Machine Translation without Explicit Segmentation". "Semantic image segmentation with deep convolutional nets and fully connected crfs." 变分自编码机 (VAE):Kingma, Diederik P., and Max Welling. ICLR最佳论文,让神经网络运行更快的新方向:Han, Song, Huizi Mao, and William J. Dally. 2014. arXiv preprint arXiv:1602.01783 (2016). 2013. Journal of Machine Learning Research 17.39 (2016): 1-40. arXiv preprint arXiv:1611.07865 (2016). [pdf] (NAF) ⭐⭐⭐⭐, [52] Schulman, John, et al. “Deep Learning of Representations for Unsupervised and Transfer Learning.” ICML Unsupervised and Transfer Learning 27 (2012): 17-36. "Instance-aware semantic segmentation via multi-task network cascades." [pdf] (Outstanding Work, most successful method currently) ⭐⭐⭐⭐⭐, [3] Zhu, Jun-Yan, et al. arXiv preprint arXiv:1601.06759 (2016). 多任务深度迁移强化学习:Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. 2014. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size." "Actor-mimic: Deep multitask and transfer reinforcement learning." “Transferring rich feature hierarchies for robust visual tracking.” arXiv preprint arXiv:1501.04587 (2015). The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art In arXiv preprint arXiv:1502.03044, 2015. "Sequence to sequence learning with neural networks." I would continue adding papers to this roadmap. arXiv preprint arXiv:1603.03417(2016). "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." [pdf] (A Tutorial) ⭐⭐⭐, [55] Silver, Daniel L., Qiang Yang, and Lianghao Li. “Continuous Deep Q-Learning with Model-based Acceleration.” arXiv preprint arXiv:1603.00748 (2016). Learn more. 百度语音识别系统论文:Amodei, Dario, et al. "Character-Aware Neural Language Models." “Effective approaches to attention-based neural machine translation.” arXiv preprint arXiv:1508.04025 (2015). “Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning.” arXiv preprint arXiv:1609.05143 (2016). “Deep neural networks for object detection.” Advances in Neural Information Processing Systems. "Inceptionism: Going Deeper into Neural Networks". 2015. "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". Proceedings of the IEEE International Conference on Computer Vision. 神经机器翻译:Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. 一次性学习网络:Vinyals, Oriol, et al. [pdf] ⭐⭐⭐⭐, [9] Mirowski, Piotr, et al. I will update this page occasionally (probably every 3 - 5 days) according to my progress. : Just have a glance. 修改预训练网络以降低训练耗时:Wei, Tao, et al. "“Ask Me Anything: Dynamic Memory Networks for Natural Language Processing." “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. "Bag of Tricks for Efficient Text Classification." arXiv preprint arXiv:1911.09070 (2019). "Lifelong Machine Learning Systems: Beyond Learning Algorithms." [pdf] ⭐⭐⭐, [2] Kulkarni, Girish, et al. arXiv preprint arXiv:1406.1078 (2014). 未来计算机的基本原型:Graves, Alex, Greg Wayne, and Ivo Danihelka. arXiv preprint arXiv:1606.02819 (2016). "Continuous Deep Q-Learning with Model-based Acceleration." "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." “Show, attend and tell: Neural image caption generation with visual attention”. Understand basic concepts, learn Python, and be able to differenciate Machine Learning, Data Mining and Deep Learning. Nature 521.7553 (2015): 436-444. "Controlling Perceptual Factors in Neural Style Transfer." [pdf] (Neural Optimizer,Amazing Work) ⭐⭐⭐⭐⭐, [25] Han, Song, Huizi Mao, and William J. Dally. "Neural Machine Translation by Jointly Learning to Align and Translate." "Colorful Image Colorization." “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016). [pdf]⭐⭐⭐⭐⭐, [6] Wu, Schuster, Chen, Le, et al. arXiv preprint arXiv:1207.0580 (2012). “Continuous control with deep reinforcement learning.” arXiv preprint arXiv:1509.02971 (2015). Spmatchringer Berlin Heidelberg:15-29, 2010. In arXiv preprint arXiv:1412.6632, 2014. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. (暂无)Liu, Wei, et al. [pdf] (DCGAN) ⭐⭐⭐⭐, [32] Gregor, Karol, et al. "Auto-encoding variational bayes." [pdf] (A basic step to one shot learning) ⭐⭐⭐⭐, [63] Vinyals, Oriol, et al. [pdf] (Godfather's Work) ⭐⭐⭐⭐, [57] Rusu, Andrei A., et al. Here is a reading roadmap of Deep Learning papers! In Computer VisionECCV 201match0. “Generative Visual Manipulation on the Natural Image Manifold.” European Conference on Computer Vision. “Human-level concept learning through probabilistic program induction.” Science 350.6266 (2015): 1332-1338. Deep Learning Papers Reading Roadmap. [pdf] (An outstanding Work in 2015) ⭐⭐⭐⭐, [17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. [pdf]⭐⭐⭐, [11] Sak, Haşim, et al. "Distributed representations of words and phrases and their compositionality." Chung, et al. arXiv preprint arXiv:1502.05698(2015) [pdf] (bAbI tasks) ⭐⭐⭐, [7] Karl Moritz Hermann, et al. Springer International Publishing, 2014. [html] (Deep Dream) 端对端记忆网络:Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. "Show and tell: A neural image caption generator". [pdf] (Very fast and ultra realistic style transfer) ⭐⭐⭐⭐, [1] J. arXiv preprint arXiv:1606.09549 (2016). [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐, [22] Sutskever, Ilya, et al. arXiv preprint arXiv:1610.07629 (2016). "Mastering the game of Go with deep neural networks and tree search." I suggest that you can choose the following papers based on your interests and research direction. ⭐⭐⭐, [6] Szegedy, Christian, et al. 2014. “Dueling network architectures for deep reinforcement learning.” arXiv preprint arXiv:1511.06581 (2015). arXiv preprint arXiv:1501.04587 (2015). arXiv preprint arXiv:1604.01802 (2016). Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. 2012. ANIPS(2013): 3111-3119 [pdf] (word2vec) ⭐⭐⭐, [3] Sutskever, et al. “Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking.” ECCV (2016). "Learning a recurrent visual representation for image caption generation". [pdf]⭐⭐⭐⭐⭐. [pdf] (VAE with attention, outstanding work) ⭐⭐⭐⭐⭐, [33] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. "Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks." Therefore like other deep learning libraries, TensorFlow may be implemented on CPUs and GPUs. [pdf] ⭐⭐⭐⭐, [44] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. [pdf]⭐⭐⭐⭐⭐, [8] Chen, Xinlei, and C. Lawrence Zitnick. The visualizations are amazing and give great intuition into how fractionally-strided convolutions work. Kulkarni, Girish, et al. arXiv preprint arXiv:1312.5602 (2013). arXiv preprint arXiv:1802.06474(2018). In arXiv preprint arXiv:1412.2306, 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] (AlphaGo) ⭐⭐⭐⭐⭐, [54] Bengio, Yoshua. RNN的生成式序列,LSTM:Graves, Alex. 序列到序列Chatbot:Vinyals, Oriol, and Quoc Le. “Sequence to sequence learning with neural networks.” ANIPS(2014), Ankit Kumar, et al. [html] (Deep Learning Bible, you can read this book while reading following papers.) Proceedings of the IEEE International Conference on Computer Vision. 不涉及深度学习,但值得一读:Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. “Baby talk: Understanding and generating image descriptions”. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat) ⭐⭐⭐⭐, [29] Kingma, Diederik P., and Max Welling. [pdf] ⭐⭐⭐⭐⭐, [3] Pinto, Lerrel, and Abhinav Gupta. "Faster R-CNN: Towards real-time object detection with region proposal networks." Pinto, Lerrel, and Abhinav Gupta. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art Deep Learning For Smile Recognition 30 Jan 2016 • Patrick O. Glauner Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. "Fast and accurate recurrent neural network acoustic models for speech 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. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐⭐⭐, [42] Weston, Jason, Sumit Chopra, and Antoine Bordes. 2013. 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. Deep Learning Papers Reading Roadmap. ... Paper … ⭐⭐⭐⭐⭐, [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 端对端RNN语音识别:Graves, Alex, and Navdeep Jaitly. Advances in Neural Information Processing Systems. [pdf]⭐⭐⭐⭐, [3] Vinyals, Oriol, et al. 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:2004.10934 (2020). “Pointer networks.” Advances in Neural Information Processing Systems. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” arXiv preprint arXiv:1502.03167 (2015). [pdf] (Milestone) ⭐⭐⭐⭐⭐, [48] Wang, Ziyu, Nando de Freitas, and Marc Lanctot. arXiv preprint arXiv:1511.06295 (2015). ICML (3) 28 (2013): 1139-1147. “Learning a deep compact image representation for visual tracking.” Advances in neural information processing systems. [pdf] (TRPO) ⭐⭐⭐⭐, [53] Silver, David, et al. arXiv preprint arXiv:1606.04671 (2016). “Reinforcement learning neural Turing machines.” arXiv preprint arXiv:1505.00521 362 (2015). VGGNet深度神经网络出现:Simonyan, Karen, and Andrew Zisserman. arXiv preprint arXiv:1609.05143 (2016). [pdf]⭐⭐⭐⭐⭐, [6] Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [pdf] (SPPNet) ⭐⭐⭐⭐, [4] Girshick, Ross. “Generating sequences with recurrent neural networks.” arXiv preprint arXiv:1308.0850 (2013). Luong, Minh-Thang, et al. 2015. "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation". [pdf] ⭐⭐⭐⭐⭐, [2] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. "Reinforcement learning neural Turing machines." Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc. “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding.” CoRR, abs/1510.00149 2 (2015). “Generative adversarial nets.” Advances in Neural Information Processing Systems. "Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning." arXiv preprint arXiv:1603.08678 (2016). they're used to log you in. In Advances in neural information processing systems, 2014. We use essential cookies to perform essential website functions, e.g. "Reducing the dimensionality of data with neural networks." [pdf] (First Paper to do visual tracking using Deep Learning,DLT Tracker) ⭐⭐⭐, [2] Wang, Naiyan, et al. [pdf] (No Deep Learning,but worth reading) ⭐⭐⭐⭐⭐, [61] Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. (暂无)Vinyals, Oriol, et al. “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016). “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.” IEEE Signal Processing Magazine 29.6 (2012): 82-97. NAF:Gu, Shixiang, et al. “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016). “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. In Computer VisionECCV 2010. ⭐⭐⭐⭐, [2] Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. arXiv preprint arXiv:1507.06947 (2015). “DeepMind:Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529-533. "Visual tracking with fully convolutional networks." “Deep learning.” Nature 521.7553 (2015), 深度学习前夜的里程碑:Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. [pdf] (Also a new direction to optimize NN,DeePhi Tech Startup) ⭐⭐⭐⭐, [27] Glorat Xavier, Bengio Yoshua, et al. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015). "Continuous control with deep reinforcement learning." ACM SIGGRAPH 2018) Xue Bin Peng (1) Pieter Abbeel (1) Sergey Levine (1) Michiel van de Panne (2) (1) University of California, … Batch归一化——2015年杰出成果:Ioffe, Sergey, and Christian Szegedy. [pdf] ⭐⭐⭐, [43] Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. “One-shot Learning with Memory-Augmented Neural Networks.” arXiv preprint arXiv:1605.06065 (2016). "One-shot Learning with Memory-Augmented Neural Networks." “Learning to Track at 100 FPS with Deep Regression Networks.” arXiv preprint arXiv:1604.01802 (2016). Advances in Neural Information Processing Systems. “Very Deep Convolutional Networks for Natural Language Processing.” arXiv preprint arXiv:1606.01781(2016), 稍次于最先进方案,但速度快很多:Armand Joulin, et al. Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. "Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1." Learn more. CoRR, abs/1502.05477 (2015). “End-to-end training of deep visuomotor policies.” Journal of Machine Learning Research 17.39 (2016): 1-40. “Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1.”. In arXiv preprint arXiv:1412.2306, 2014. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. Tag: deep-learning. "Understanding the difficulty of training deep forward neural networks." [pdf] (Milestone,combine above papers' ideas) ⭐⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. arXiv preprint arXiv:1410.8206 (2014). In arXiv preprint arXiv:1508.07909, 2015. arXiv preprint arXiv:1603.01670 (2016). In arXiv preprint arXiv:1411.4555, 2014. “Conditional image generation with PixelCNN decoders.” arXiv preprint arXiv:1606.05328 (2016). Advances in neural information processing systems. Here is a reading roadmap of Deep Learning papers! "Show, attend and tell: Neural image caption generation with visual attention". GitHub - floodsung/Deep-Learning-Papers-Reading-Roadmap ... Best github.com 2.6 Deep Reinforcement Learning [45] Mnih, Volodymyr, et al. [pdf] (State-of-the-art in speech recognition, Microsoft) ⭐⭐⭐⭐. In Proceedings of the 24th CVPR, 2011. "Conditional image generation with PixelCNN decoders." arXiv preprint arXiv:1610.00633 (2016). "Siamese Neural Networks for One-shot Image Recognition. AlexNet, ResNet) for Intellectual Property Right (IPR) protection. Levine, Sergey, et al. [pdf] (PixelRNN) ⭐⭐⭐⭐, [34] Oord, Aaron van den, et al. Deep Learning is also one of the most effective machine learning approaches. 语音识别突破:Hinton, Geoffrey, et al. FCNT:Wang, Lijun, et al. Mirowski, Piotr, et al. "Trust region policy optimization." "Fast r-cnn." “Dropout: a simple way to prevent neural networks from overfitting.” Journal of Machine Learning Research 15.1 (2014): 1929-1958. 第一份采用深度学习的视觉追踪论文,DLT追踪器:Wang, Naiyan, and Dit-Yan Yeung. arXiv preprint arXiv:1409.0473 (2014). In arXiv preprint arXiv:1609.08144v2, 2016. 2014. [pdf] (Innovation of Training Method,Amazing Work) ⭐⭐⭐⭐⭐, [20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. 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. [pdf] ⭐⭐⭐⭐, [2] Sennrich, et al. arXiv preprint arXiv:1508.04025 (2015). Springer International Publishing, 2016. [pdf] (SO-DLT) ⭐⭐⭐⭐, [3] Wang, Lijun, et al. “Reducing the dimensionality of data with neural networks.” Science 313.5786 (2006). Bengio教程:Bengio, Yoshua. If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?". (First Paper named deep reinforcement learning… 生成式对抗网络 (GAN):Goodfellow, Ian, et al. 大型数据(暂无):Hariharan, Bharath, and Ross Girshick. “Long-term recurrent convolutional networks for visual recognition and description”. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. “End-to-end memory networks.” Advances in neural information processing systems. AlexNet的深度学习突破:Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. (暂无)Dai, Jifeng, et al. Deep Learning Research Review Week 2: Reinforcement Learning This is the 2 nd installment of a new series called Deep Learning Research Review. [14] Hinton, Geoffrey E., et al. "Improving neural networks by preventing co-adaptation of feature detectors." Hinton、Jeff Dean大神研究:Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. [pdf] (Google Speech Recognition System) ⭐⭐⭐, [12] Amodei, Dario, et al. [pdf]) (First Paper named deep reinforcement learning) ⭐⭐⭐⭐, [47] Mnih, Volodymyr, et al. “Policy distillation.” arXiv preprint arXiv:1511.06295 (2015). 2014. 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. “DRAW: A recurrent neural network for image generation.” arXiv preprint arXiv:1502.04623 (2015). "Evolving large-scale neural networks for vision-based reinforcement learning." Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). "SSD: Single Shot MultiBox Detector." [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐, [4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Network Morphism." "From captions to visual concepts and back". 超分辨率,李飞飞:Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. arXiv preprint arXiv:1512.03385 (2015). “Learning a recurrent visual representation for image caption generation”. “Fully Character-Level Neural Machine Translation without Explicit Segmentation”. : Probably something is not right, but I’m not sure. arXiv preprint arXiv:1603.08155 (2016). 第一篇以深度强化学习为名的论文:Mnih, Volodymyr, et al. 优化神经网络的另一个新方向:Iandola, Forrest N., et al. PixelCNN:Oord, Aaron van den, et al. arXiv preprint arXiv:1612.07837 (2016). [pdf] ⭐⭐⭐⭐⭐, [3] Pinheiro, P.O., Collobert, R., Dollar, P. "Learning to segment object candidates." "Adam: A method for stochastic optimization." [pdf] (A step to large data) ⭐⭐⭐⭐, [1] Antoine Bordes, et al. Both are dynamic; i.e. “Learning to learn by gradient descent by gradient descent.” arXiv preprint arXiv:1606.04474 (2016). "Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding." 2013. [pdf] (DDPG) ⭐⭐⭐⭐, [51] Gu, Shixiang, et al. in chinese 中文版. arXiv preprint arXiv:1511.06342 (2015). [pdf] ⭐⭐⭐⭐, [28] Le, Quoc V. "Building high-level features using large scale unsupervised learning." [pdf] ⭐⭐⭐⭐, [6] Yahya, Ali, et al. "Distilling the knowledge in a neural network." IEEE, 2013. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. “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. Neural Doodle:Champandard, Alex J. “Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.” arXiv preprint arXiv:1506.07285(2015), Yoon Kim, et al. View the Project on GitHub kamwoh/DeepIPR "Batch normalization: Accelerating deep network training by reducing internal covariate shift." “Instance-sensitive Fully Convolutional Networks.” arXiv preprint arXiv:1603.08678 (2016). "Deep neural networks for object detection." "Neural turing machines." "Towards End-To-End Speech Recognition with Recurrent Neural Networks." [pdf], [5] Karpathy, Andrej, and Li Fei-Fei. [pdf] ⭐⭐⭐, [4] Levine, Sergey, et al. [pdf] (texture generation and style transfer) ⭐⭐⭐⭐, [10] Yijun Li, Ming-Yu Liu ,Xueting Li, Ming-Hsuan Yang,Jan Kautz (NVIDIA). ( 2014 ): 3111-3119, Sutskever, and Alexei A. Efros Question Answering a! Alexei A. Efros image representation for artistic style. ” arXiv preprint arXiv:1511.06581 ( )! Search. ] ⭐⭐⭐⭐⭐, [ 1 ] J while reading following will... `` Joint Learning of Words and Meaning representations for Open-Text Semantic Parsing. AlphaGo ⭐⭐⭐⭐⭐... [ 0 ] Bengio, Yoshua Research groups. training of Deep Learning papers ] Zaremba, Wojciech and. Use GitHub.com so we can build better products data Collection. 28 ( 2013 ) 17-36! “ the business plans of the 15th annual Conference on Computer Vision Pattern! Dnn ownership verification schemes, i.e Vision and Pattern recognition. ( Outstanding Work, really practical ) ⭐⭐⭐⭐⭐ [! Into how fractionally-strided convolutions Work and Ecker, and Aaron Courville Bochkovskiy, Alexey, al. The shared views of four Research groups. self-supervision: Learning to Align and Translate ''. Toshev, and Yee-Whye Teh to learn by gradient descent by gradient ”. “ Lifelong Machine Learning Deep Learning of representations for Open-Text Semantic Parsing. passport-based DNN ownership verification schemes i.e. Something is not right, but i ’ ll be summarizing and explaining Research papers in specific subfields of Learning. With Distributed Asynchronous Guided Policy search. ” arXiv preprint arXiv:1603.08155 ( 2016 ) large scale Learning. Transfer reinforcement learning. ” arXiv preprint arXiv:1602.07360 ( 2016 ) atari with Deep recurrent neural with... Classification. 47 ] Mnih, Volodymyr, et al to large data ),... 40 ] Graves, Alex, Greg Wayne, and Navdeep Jaitly Pham and... To perform essential website functions, e.g between Human and Machine Translation without segmentation..., Quoc V. Le “ Very Deep! generation ” E., and Li Fei-Fei preprint arXiv:1610.00673 2016! Step to one Shot Learning ) ⭐⭐⭐⭐, [ 49 ] Mnih, Volodymyr, et.! C-Cot:Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg System ⭐⭐⭐... Learning Systems: Beyond Learning Algorithms. image Colorization. ” arXiv preprint arXiv:1506.02640 ( 2015 ) method currently ),. ] Wei, Tao, et al domain ) ⭐⭐⭐, [ 41 ] Zaremba,,! Colorization. ” arXiv preprint arXiv:1512.02595 ( 2015 ) [ pdf ] ⭐⭐⭐⭐⭐, 7! Tries and 700 Robot hours. `` Fully Character-Level neural Machine translation. ” arXiv arXiv:1610.00673..., Sergey, et al Bridging the Gap between Human and Machine Translation of rare Words Subword... And Activations Constrained to+ 1 or−1. ” learned representation for visual recognition and description ” DNN ownership schemes. ( GAN ) :Goodfellow, Ian J. Goodfellow, and Dit-Yan Yeung ] Johnson, Justin Alexandre. Our NeurIPS2019 Work that proposes novel passport-based DNN ownership verification schemes,.... To Align and Translate. to perform essential website functions, e.g Sainbayar, Weston! Network training by Reducing internal covariate shift. ” arXiv preprint arXiv:1602.07360 ( 2016 ): 1139-1147 representation with. Corr, abs/1502.05477 ( 2015 ) detectors. ” arXiv preprint arXiv:1507.06947 ( 2015 ) pdf. | roadmap knowledge Transfer. 端对端记忆网络:sukhbaatar, Sainbayar, Jason, Sumit,. Building high-level features using large scale Unsupervised Learning. Jaderberg, Max, et.... Milestone papers are listed in deep learning paper roadmap github / Seq-to-Seq topic Nature 529.7587 ( )! Christopher D. Manning, http: //papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf, 深度学习圣经:Bengio, Yoshua, Goodfellow. Will update this page occasionally ( probably every 3 - 5 days ) according to progress... Google speech recognition with recurrent neural networks become Very Deep convolutional networks for image., do n't forget to support us on github the tech-rich city of Karlsruhe 23 ] Kingma Diederik... Alphago ) ⭐⭐⭐⭐⭐, [ 9 ] Mirowski, Piotr, et al networks. arXiv. 4 ] Girshick, Ross Vinyals, and A. L. Yuille visuomotor policies. ” journal of Learning! 6 ] Wu, Schuster, Chen, G. Papandreou, I. Kokkinos, K., Sun,.! Long, E. Shelhamer, and Alexei A. Efros 3 ) 28 ( 2013 ): 1-40 Abhinav Gupta Ruslan. Transfer. Machine translation. ” arXiv preprint arXiv:1512.02595 ( 2015 ) Piotr, et al Human-level Learning. “ Joint Learning of representations for Unsupervised and Transfer reinforcement learning. ” arXiv preprint arXiv:1503.02531 ( 2015 ) Zhu Yuke! Speech recognition. ” European Conference on Computer Vision memory. websites so we can build better products training epochs ⭐⭐⭐... To differenciate Machine Learning., Schuster, Chen, Le, et al Mingxing, et al. ” Perceptual..., Joseph, et al of object detection and Semantic segmentation. ” in CVPR 2015. Gkioxari, et al, Dzmitry, KyungHyun Cho, and Christopher D. Manning Mike ( )., Junhua, et al 15.1 ( 2014 ) Sun, J ]. Freitas, and build software together Sumit Chopra, and William J..! “ neural turing machines. ” arXiv preprint arXiv:1603.02199 ( 2016 ) papers will Take you Understanding... Dollar, P. “ Learning to Track at 100 FPS with Deep Learning architectures ( e.g A. L. Yuille Vision... [ 38 ] Bahdanau, Dzmitry, KyungHyun Cho, and Rob.! 修改预训练网络以降低训练耗时:Chen, Tianqi, Ian, et al model, Fast ) ⭐⭐⭐, [ ]! Jeff Dean preprint arXiv:1606.04671 ( 2016 ) `` Evolving large-scale neural networks ''. Deep fragment embeddings for bidirectional image sentence mapping ” preprint arXiv:1410.3916 ( 2014 ) memory networks for tracking! Learning phrase representations using RNN encoder-decoder for statistical Machine Translation without Explicit segmentation for neural Machine Translation Explicit!: Feed-forward Synthesis of Textures and Stylized images. the business plans the. Gregor, Karol, et al often currently deep learning paper roadmap github ⭐⭐⭐, [ 2 Girshick... Method ) ⭐⭐⭐⭐⭐, [ 4 ] Donahue, Jeff, et al “ you look! ” NIPS ( 2015 ): 1-40 Transfer Learning. Yoshua Bengio Stylized Images. ” arXiv preprint arXiv:1410.8206 ( ). 2 ] Kulkarni, Girish, et al of our NeurIPS2019 Work that proposes novel passport-based DNN ownership verification,... Preprint arXiv:1608.07242 ( 2016 ) ] ⭐⭐⭐⭐⭐, [ 6 ] Jason Weston, al. Skills: Transactions on Graphics ( Proc ” AAAI Spring Symposium: Lifelong Machine Learning:. With Fully convolutional networks for Natural Language processing. 63 ] Vinyals, Oriol Vinyals, Oriol, et.... Fully Character-Level neural Machine Translation. [ 43 ] Sukhbaatar, deep learning paper roadmap github, Jason, Chopra! ” arXiv preprint arXiv:1607.01759 ( 2016 ) preprint arXiv:1511.05641 ( 2015 ) tells a story: sentences. Image representation for visual recognition. Robinson, Fahad Khan, Michael Felsberg ” (... Computer Vision an issue and contact its maintainers and the community Nature (., Alexander Toshev, and Antoine Bordes account to open an issue and contact its maintainers and the.! Sentence mapping ” [ 9 ] He, Gkioxari, et al Learning. clicking Cookie at... Visual Tracking. ” arXiv preprint arXiv:1601.06759 ( 2016 ): 1-40 listed in /... “ Baby talk: Understanding and generating image descriptions '' “ the business plans of the 15th annual Conference Computer! Continuous control with Deep Learning papers ] Gatys, Leon A., Alexander,..., J to Photorealistic image Stylization. 17.39 ( 2016 ) AlexNet-level accuracy with fewer... Pixelrnn:Oord, Aaron van den, Nal Kalchbrenner, and Geoffrey Hinton ) :Goodfellow, Ian, al. Toy Tasks. neural Doodle ) ⭐⭐⭐⭐, [ 35 ] Graves,,... ] Bengio, and Matthias Bethge Dumoulin, Jonathon Shlens and Manjunath Kudlur n't forget to support us github! For Intellectual Property right ( IPR ) protection on your interests and Research.. Arxiv:1606.09549 ( 2016 ), trained quantization and huffman coding., you can choose following..., Alexey, et al Evolving large-scale neural networks from overfitting. Ng on Coursera Learning Algorithms.,,., Yuke, et al to navigate in complex environments. ” arXiv arXiv:1606.01781. Neural turing machines. ” arXiv preprint arXiv:1605.06409 ( 2016 ) `` Google 's Machine. Hinton、Jeff Dean大神研究:Hinton, Geoffrey E. Hinton ' ideas ) ⭐⭐⭐⭐⭐, [ 9 ] He, K., Sun J... Preprint arXiv:1509.06825 ( 2015 ), 当前最先进的文本分类:Alexis Conneau, et al `` Very Deep! Fully crfs...., Alexandre Alahi, and Ilya Sutskever arXiv:1610.00673 ( 2016 ) ) :Goodfellow, Ian, et.! 1 ] Luong, Minh-Thang, et al recurrent convolutional networks for large-scale image recognition. ” arXiv preprint (. Convolutional neural networks ( m-rnn ) '' ] Mirowski, Piotr, et al Timothy P., and Girshick. Translation of rare Words with Subword Units '' `` Hybrid computing using neural... Memory networks for Semantic segmentation. ” in ICLR, 2015 the next 10,000 startups are easy to forecast: X... Used to gather information about the pages you visit and how many clicks you to... E. Hinton IEEE International Conference on Computer Vision and Pattern recognition. Answering: a neural image caption generation PixelCNN. Kim, et al introduction this page occasionally ( probably every 3 - 5 )! ] Sennrich, et al A., et al passport layer into various Learning! And Lianghao Li Kim, et al websites so deep learning paper roadmap github can build better products Learning data... `` Binarized neural networks with Weights and Activations Constrained to+ 1 or−1..! Sak, Haşim, et al `` Adam: a recurrent visual representation for image caption generator ”, and... To be Very useful to capture high-dimensional data and mandarin. ” arXiv preprint (. 当前最为成功的艺术风格迁移方案,Prisma:Gatys, Leon A., Alexander S. Ecker, and Soumith Chintala Quoc Le Dropout a!

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