01/23/2019 (Introduction, Backprop, Shallow Vs Deep, Policies) |
Required
- Reducing the dimensionality of data with neural networks by Hinton et. al. (2006) Here
- How to read a technical paper by Jason Eisner (2009) [HTML]
- Chapter 6 of Deep Learning Book by Goodfellow Here
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Optional
- Chapters 1-5 of Deep Learning Book by Goodfellow Here
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01/30/2019 (Convolutional Neural Networks, Vision (ImageNet) ) |
Required
- Chapter 9 (Convolutional Neural Networks) of Deep Learning Book by Goodfellow Here
- AlexNet
(Presenter: )
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Paper Critique and Summary before Class I (Read 1 out of the 3)
- VGGNet Here
- GoogleNet Here
- ResNet Here
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Optional
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02/06/2019 (Advanced Training) |
Required
- Dropout: a simple way to prevent neural networks from overfitting by Srivastava et. al. Here
- Chapters 7 and 8 of Deep Learning Book by Goodfellow Here
- Net2net: Accelerating learning via knowledge transfer." arXiv preprint arXiv:1511.05641 (2015) by Chen et. al. Here (Presenter: )
- Adam: A method for stochastic optimization by Diederik et. al. (2014) Here (Presenter: )
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Optional
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Sec 2.2 of DL Papers Reading Roadmap: Here
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Sec 2.1 of DL Papers Reading Roadmap: Here
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02/13/2019 (Unsupervised Learning/Deep Generative Models) |
Required
- Generative Adverserial Networks by Goodfellow (2014) Here (Presenter: )
- UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS by Radford et. al. (2015) (Presenter: ) Here
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Optional
- Chapter 14 (Autoencoders) of deep learning book Here
- Papers on Unsupervised/Generative learning Here
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02/20/2019 (Sequence Modeling) |
Required
- Chapter 10 of Deep Learning Book on Sequence Modeling Here
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Paper Critique and Summary before Class II (Read 1 out of the 4)
- Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013). by Graves Here
- Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014 by Sutskever (2014) Here
- Visualizing and Understanding Recurrent Neural Networks by Karpathy et. al. ICLR (2015) Here
- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs (NIPS 2017) by Wang et. al. Here
- Gated Feedback Recurrent Neural Networks by Chung et. al. (2015) Here
Optional
- Papers on Sequence Modeling Here
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02/27/2019 (Applications: NLP ) |
Required
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Distributed Representations of Words and Phrases and their Compositionality by Mikolov et. al. (2013) Here
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Paper Critique and Summary before Class III (Read 1 out of the 4)
- Attention is All you Need by Vaswani et. al. (2017) Here
- Ask Me Anything:Dynamic Memory Networks for Natural Language Processing by Kumar et. al. (2016) Here
- Global Vectors for Word Representation by Pennington et. al. Here
- Adversarial Multi-task Learning for Text Classification by Liu et. al. (2017)
- An Empirical Exploration of Recurrent Network Architectures by Jozefowicz et. al. 2015 Here
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Optional
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Efficient Estimation of Word Representations in Vector Space Here
- See class by Richard Socher Here
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03/06/2019 (Applications: Recommender Systems) |
Required
- Deep Learning based Recommender System: A Survey and New Perspectives by Zhang et. al. (2018) Here (Presenter: )
- Neural Collaborative Filtering by He et. al. (2017) Here (Presenter: )
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Optional
- Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention by Chen et. al. 2017 Here
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03/20/2019 (Deep Learning in X) |
Required
- Opportunities and obstacles for deep learning in biology and medicine by Ching et. al. (2018) Here (Presenter: )
- A Neural Algorithm of Artistic Style by Gatys et. al. (2015) Here (Presenter: )
- Deep Learning in Medical Image Analysis by Shen et. al. (2017) Here (Presenter: )
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Optional
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03/27/2019 (Deep Reinforcement Learning) |
Required
- Human-level control through deep reinforcement learning by Mnih et. al. (2015) Here
- Dueling network architectures for deep reinforcement learning by Ziyu et. al. (2015) Here
- Mastering the game of Go with deep neural networks and tree search." Nature 529.7587 (2016): 484-489 Here
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Optional
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04/03/2019 (Transfer/One-Shot DL) |
Required
- Progressive Neural Networks by Rusu et. al. (2016) Here (Presenter: )
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention (2016) by Xu. et. al. Here (Presenter: )
- How Transferable are features in deep neural networks Here
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Optional
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04/10/2019 (Resource-Aware Deep Learning) |
Required
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey by Mohammadi et. al. Here
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Paper Critique and Summary before Class IV (Read 1 out of the 4)
- Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding by Song et. al. (2015) Here
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and less than 1MB model size. arXiv preprint arXiv:1602.07360 by Forrest et. al. (2016). Here
- Deep Learning for the Internet of Things by Yao et. al. (2018) Here
- Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1 by Matthieu et. al. Here
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Optional
- Papers by Yao (UIUC) Here
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04/17/2019 (Free/Work on Projects) |
04/24/2019 (Project Presentations I) |
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05/01/2019 (Project Presentations II) |
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