机器学习阅读论文序列

论文序列

已读

[pdf] [doi] LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521(7553), pp. 436–444. doi:10.1038/nature14539. 概论 Nature Review
[pdf] [src] Krizhevsky, A., Sutskever, I. and Hinton G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(2), pp. 1090-1098. Available at: src (Accessed: 17 August 2022). 图片分类 AlexNet
[pdf] [doi] He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. doi: 10.1109/CVPR.2016.90. 残差网络 ResNet CVPR best paper
[pdf] [doi] Hinton, Geoffrey, et al. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), pp. 82-97. doi: 10.1109/MSP.2012.2205597.

未分类

Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013.
Amodei, Dario, et al. Deep speech 2: End-to-end speech recognition in english and mandarin. arXiv preprint arXiv:1512.02595 (2015).
W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig Achieving Human Parity in Conversational Speech Recognition. arXiv preprint arXiv:1610.05256 (2016).
Srivastava, Nitish, et al. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15.1 (2014): 1929-1958. [pdf]
Ioffe, Sergey, and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015). [pdf] (An outstanding Work in 2015)
Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450 (2016). [pdf] (Update of Batch Normalization)
Jaderberg, Max, et al. Decoupled neural interfaces using synthetic gradients. arXiv preprint arXiv:1608.05343 (2016). [pdf] (Innovation of Training Method,Amazing Work)
Sutskever, Ilya, et al. On the importance of initialization and momentum in deep learning. ICML (3) 28 (2013): 1139-1147. [pdf] (Momentum optimizer)
Kingma, Diederik, and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). [pdf] (Maybe used most often currently)
Andrychowicz, Marcin, et al. Learning to learn by gradient descent by gradient descent. arXiv preprint arXiv:1606.04474 (2016). [pdf] (Neural Optimizer,Amazing Work)
Han, Song, Huizi Mao, and William J. Dally. Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. CoRR, abs/1510.00149 2 (2015). [pdf] (ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup)
Iandola, Forrest N., et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size. arXiv preprint arXiv:1602.07360 (2016).
Le, Quoc V. Building high-level features using large scale unsupervised learning. 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013. [pdf] (Milestone, Andrew Ng, Google Brain Project, Cat)
Kingma, Diederik P., and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013). [pdf] (VAE)
Goodfellow, Ian, et al. Generative adversarial nets. Advances in Neural Information Processing Systems. 2014. [pdf] (GAN,super cool idea)
Radford, Alec, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015). [pdf] (DCGAN)
Gregor, Karol, et al. DRAW: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623 (2015). [pdf] (VAE with attention, outstanding work)
Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759 (2016). [pdf] (PixelRNN)
Oord, Aaron van den, et al. Conditional image generation with PixelCNN decoders. arXiv preprint arXiv:1606.05328 (2016). [pdf] (PixelCNN)
S. Mehri et al., SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. arXiv preprint arXiv:1612.07837 (2016).
Graves, Alex. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013). [pdf] (LSTM, very nice generating result, show the power of RNN)
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. Advances in neural information processing systems. 2014. [pdf] (Outstanding Work)
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. Advances in neural information processing systems. 2014. [pdf] (Outstanding Work)
Vinyals, Oriol, and Quoc Le. A neural conversational model. arXiv preprint arXiv:1506.05869 (2015). [pdf] (Seq-to-Seq on Chatbot)
Graves, Alex, Greg Wayne, and Ivo Danihelka. Neural turing machines. arXiv preprint arXiv:1410.5401 (2014). [pdf] (Basic Prototype of Future Computer)
Graves, Alex, et al. Hybrid computing using a neural network with dynamic external memory. Nature (2016). [pdf] (Milestone,combine above papers' ideas)
Mnih, Volodymyr, et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013). [pdf]) (First Paper named deep reinforcement learning)
Mnih, Volodymyr, et al. Human-level control through deep reinforcement learning. Nature 518.7540 (2015): 529-533. [pdf] (Milestone)
Mnih, Volodymyr, et al. Asynchronous methods for deep reinforcement learning. arXiv preprint arXiv:1602.01783 (2016). [pdf] (State-of-the-art method)
Silver, David, et al. Mastering the game of Go with deep neural networks and tree search. Nature 529.7587 (2016): 484-489. [pdf] (AlphaGo)
Rusu, Andrei A., et al. Progressive neural networks. arXiv preprint arXiv:1606.04671 (2016). [pdf] (Outstanding Work, A novel idea)
Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. Human-level concept learning through probabilistic program induction. Science 350.6266 (2015): 1332-1338. [pdf] (No Deep Learning,but worth reading)
Antoine Bordes, et al. Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing. AISTATS(2012)
Ankit Kumar, et al. ****Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.** arXiv preprint arXiv:1506.07285(2015)
Girshick, Ross, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. [pdf] (RCNN)
Redmon, Joseph, et al. You only look once: Unified, real-time object detection. arXiv preprint arXiv:1506.02640 (2015). [pdf] (YOLO,Oustanding Work, really practical)
Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., Kaiser L., and Polosukhin I. Attention is all you need. arXiv, 2017.
Tan, Mingxing, et al. EfficientDet: Scalable and Efficient Object Detection. arXiv preprint arXiv:1911.09070 (2019).
Karpathy, Andrej, and Li Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In arXiv preprint arXiv:1412.2306, 2014.
Fang, Hao, et al. From captions to visual concepts and back. In arXiv preprint arXiv:1411.4952, 2014.
Xu, Kelvin, et al. Show, attend and tell: Neural image caption generation with visual attention. In arXiv preprint arXiv:1502.03044, 2015.
Lee, et al. Fully Character-Level Neural Machine Translation without Explicit Segmentation. In arXiv preprint arXiv:1610.03017, 2016.
Levine, Sergey, et al. End-to-end training of deep visuomotor policies. Journal of Machine Learning Research 17.39 (2016): 1-40.
Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576 (2015). [pdf] (Outstanding Work, most successful method currently)
J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation. in CVPR, 2015. [pdf]
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In ICLR, 2015.