Tổng hợp tài liệu mà mình đọc để nghiên cứu machine learning ?
Tài liệu này mình sưu tầm và copy ở đâu đó trên mạng hình như trong 1 tweet trên tweeter của ai cũng chả nhớ nhưng mà họ có tổng hợp rất nhiều paper và tài liệu quan trọng mà các bạn làm AI phải biết .
ngoài ra các bạn có thể tham khảo tài liệu của SKlearn
Hoặc của tensorflow Link nào die thì các bạn c
- Machine Learning
- Deep Learning
- Understanding
- Optimization / Training Techniques
- Unsupervised / Generative Models
- Image Segmentation / Object Detection
- Image / Video
- Natural Language Processing
- Speech / Other
- Reinforcement Learning
- New papers
- Classic Papers
Understanding
- Distilling the knowledge in a neural network(2015), G. Hinton et al. pdf
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images(2015), A. Nguyen et al. pdf
- How transferable are features in deep neural networks?(2014), J. Yosinski et al. pdf
- CNN features off-the-Shelf: An astounding baseline for recognition(2014), A. Razavian et al. pdf
- Learning and transferring mid-Level image representations using convolutional neural networks(2014), M. Oquab et al. pdf
- Visualizing and understanding convolutional networks(2014), M. Zeiler and R. Fergus pdf
- Decaf: A deep convolutional activation feature for generic visual recognition(2014), J. Donahue et al. pdf
Optimization / Training Techniques
- Batch normalization: Accelerating deep network training by reducing internal covariate shift(2015), S. Loffe and C. Szegedy pdf
- Delving deep into rectifiers: Surpassing human-level performance on imagenet classification(2015), K. He et al. pdf
- Dropout: A simple way to prevent neural networks from overfitting(2014), N. Srivastava et al. pdf
- Adam: A method for stochastic optimization(2014), D. Kingma and J. Ba pdf
- Improving neural networks by preventing co-adaptation of feature detectors(2012), G. Hinton et al. pdf
- Random search for hyper-parameter optimization(2012) J. Bergstra and Y. Bengio pdf
Unsupervised / Generative Models
- Pixel recurrent neural networks(2016), A. Oord et al. pdf
- Improved techniques for training GANs(2016), T. Salimans et al. pdf
- Unsupervised representation learning with deep convolutional generative adversarial networks(2015), A. Radford et al. pdf
- DRAW: A recurrent neural network for image generation(2015), K. Gregor et al. pdf
- Generative adversarial nets(2014), I. Goodfellow et al. pdf
- Auto-encoding variational Bayes(2013), D. Kingma and M. Welling pdf
- Building high-level features using large scale unsupervised learning(2013), Q. Le et al. pdf
Image Segmentation / Object Detection
- You only look once: Unified, real-time object detection(2016), J. Redmon et al. pdf
- Fully convolutional networks for semantic segmentation(2015), J. Long et al. pdf
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(2015), S. Ren et al. pdf
- Fast R-CNN(2015), R. Girshick pdf
- Rich feature hierarchies for accurate object detection and semantic segmentation(2014), R. Girshick et al. pdf
- Semantic image segmentation with deep convolutional nets and fully connected CRFs, L. Chen et al. pdf
- Learning hierarchical features for scene labeling(2013), C. Farabet et al. pdf
Image / Video
- Image Super-Resolution Using Deep Convolutional Networks(2016), C. Dong et al. pdf
- A neural algorithm of artistic style(2015), L. Gatys et al. pdf
- Deep visual-semantic alignments for generating image descriptions(2015), A. Karpathy and L. Fei-Fei pdf
- Show, attend and tell: Neural image caption generation with visual attention(2015), K. Xu et al. pdf
- Show and tell: A neural image caption generator(2015), O. Vinyals et al. pdf
- Long-term recurrent convolutional networks for visual recognition and description(2015), J. Donahue et al. pdf
- VQA: Visual question answering(2015), S. Antol et al. pdf
- DeepFace: Closing the gap to human-level performance in face verification(2014), Y. Taigman et al. pdf:
- Large-scale video classification with convolutional neural networks(2014), A. Karpathy et al. pdf
- DeepPose: Human pose estimation via deep neural networks(2014), A. Toshev and C. Szegedy pdf
- Two-stream convolutional networks for action recognition in videos(2014), K. Simonyan et al. pdf
- 3D convolutional neural networks for human action recognition(2013), S. Ji et al. pdf
Natural Language Processing
- Neural Architectures for Named Entity Recognition(2016), G. Lample et al. pdf
- Exploring the limits of language modeling(2016), R. Jozefowicz et al. pdf
- Teaching machines to read and comprehend(2015), K. Hermann et al. pdf
- Effective approaches to attention-based neural machine translation(2015), M. Luong et al. pdf
- Conditional random fields as recurrent neural networks(2015), S. Zheng and S. Jayasumana. pdf
- Memory networks(2014), J. Weston et al. pdf
- Neural turing machines(2014), A. Graves et al. pdf
- Neural machine translation by jointly learning to align and translate(2014), D. Bahdanau et al. pdf
- Sequence to sequence learning with neural networks(2014), I. Sutskever et al. pdf
- Learning phrase representations using RNN encoder-decoder for statistical machine translation(2014), K. Cho et al. pdf
- A convolutional neural network for modeling sentences(2014), N. Kalchbrenner et al. pdf
- Convolutional neural networks for sentence classification(2014), Y. Kim pdf
- Glove: Global vectors for word representation(2014), J. Pennington et al. pdf
- Distributed representations of sentences and documents(2014), Q. Le and T. Mikolov pdf
- Distributed representations of words and phrases and their compositionality(2013), T. Mikolov et al. pdf
- Efficient estimation of word representations in vector space(2013), T. Mikolov et al. pdf
- Recursive deep models for semantic compositionality over a sentiment treebank(2013), R. Socher et al. pdf
- Generating sequences with recurrent neural networks(2013), A. Graves. pdf
Speech / Other
- End-to-end attention-based large vocabulary speech recognition(2016), D. Bahdanau et al. pdf
- Deep speech 2: End-to-end speech recognition in English and Mandarin(2015), D. Amodei et al. pdf
- Speech recognition with deep recurrent neural networks(2013), A. Graves pdf
- Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups(2012), G. Hinton et al. pdf
- Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition(2012) G. Dahl et al. pdf
- Acoustic modeling using deep belief networks(2012), A. Mohamed et al. pdf
Reinforcement Learning
- End-to-end training of deep visuomotor policies(2016), S. Levine et al. pdf
- Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection(2016), S. Levine et al. pdf
- Asynchronous methods for deep reinforcement learning(2016), V. Mnih et al. pdf
- Deep Reinforcement Learning with Double Q-Learning(2016), H. Hasselt et al. pdf
- Mastering the game of Go with deep neural networks and tree search(2016), D. Silver et al. pdf
- Continuous control with deep reinforcement learning(2015), T. Lillicrap et al. pdf
- Human-level control through deep reinforcement learning(2015), V. Mnih et al. pdf
- Deep learning for detecting robotic grasps(2015), I. Lenz et al. pdf
- Playing atari with deep reinforcement learning(2013), V. Mnih et al. pdf)
New papers
- Deep Photo Style Transfer(2017), F. Luan et al. pdf
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning(2017), T. Salimans et al. pdf
- Deformable Convolutional Networks(2017), J. Dai et al. pdf
- Mask R-CNN(2017), K. He et al. pdf
- Learning to discover cross-domain relations with generative adversarial networks(2017), T. Kim et al. pdf
- Deep voice: Real-time neural text-to-speech(2017), S. Arik et al., pdf
- PixelNet: Representation of the pixels, by the pixels, and for the pixels(2017), A. Bansal et al. pdf
- Batch renormalization: Towards reducing minibatch dependence in batch-normalized models(2017), S. Ioffe. pdf
- Wasserstein GAN(2017), M. Arjovsky et al. pdf
- Understanding deep learning requires rethinking generalization(2017), C. Zhang et al. pdf
- Least squares generative adversarial networks(2016), X. Mao et al. pdf
Classic Papers
- An analysis of single-layer networks in unsupervised feature learning(2011), A. Coates et al. pdf
- Deep sparse rectifier neural networks(2011), X. Glorot et al. pdf
- Natural language processing(almost) from scratch(2011), R. Collobert et al. pdf
- Recurrent neural network based language model(2010), T. Mikolov et al. pdf
- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion(2010), P. Vincent et al. pdf
- Learning mid-level features for recognition(2010), Y. Boureau pdf
- A practical guide to training restricted boltzmann machines(2010), G. Hinton pdf
- Understanding the difficulty of training deep feedforward neural networks(2010), X. Glorot and Y. Bengio pdf
- Why does unsupervised pre-training help deep learning(2010), D. Erhan et al. pdf
- Learning deep architectures for AI(2009), Y. Bengio. pdf
- Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations(2009), H. Lee et al. pdf
- Greedy layer-wise training of deep networks(2007), Y. Bengio et al. pdf
- A fast learning algorithm for deep belief nets(2006), G. Hinton et al. pdf
- Gradient-based learning applied to document recognition(1998), Y. LeCun et al. pdf
- Long short-term memory(1997), S. Hochreiter and J. Schmidhuber. pdf
Written on May 6, 2019