├── Final ├── Sample Problem 1.pdf └── Sample Problem 2.pdf ├── HW ├── HW1 │ ├── .Rhistory │ ├── Day2.docx │ ├── Homework1.pdf │ ├── Homework1_2014012684.Rmd │ └── Homework1_2014012684.pdf ├── HW10 │ ├── .Rhistory │ ├── Homework10_2014012684.Rmd │ └── Homework10_2014012684.pdf ├── HW2 │ ├── .Rhistory │ ├── Homework2_2014012684.Rmd │ └── Homework2_2014012684.pdf ├── HW3 │ ├── .Rhistory │ ├── HW3.pdf │ ├── Homework3_2014012684.Rmd │ └── Homework3_2014012684.pdf ├── HW4 │ ├── .Rhistory │ ├── HW4.pdf │ ├── Homework4_2014012684.Rmd │ ├── Homework4_2014012684.pdf │ ├── problem set 2.pdf │ └── problem set.pdf ├── HW5 │ ├── .Rhistory │ ├── Homework5_2014012684.Rmd │ └── Homework5_2014012684.pdf ├── HW6 │ ├── .Rhistory │ ├── Homework6_2014012684.Rmd │ └── Homework6_2014012684.pdf ├── HW7 │ ├── Homework7_2014012684.Rmd │ └── Homework7_2014012684.pdf ├── HW8 │ ├── .Rhistory │ ├── Homework8_2014012684.Rmd │ └── Homework8_2014012684.pdf └── HW9 │ ├── .Rhistory │ ├── Homework9_2014012684.Rmd │ └── Homework9_2014012684.pdf ├── Notes ├── .Rhistory ├── Discussion1.Rmd ├── Discussion1.pdf ├── Lecture1.docx ├── Lecture1.pdf ├── Lecture10.pdf ├── Lecture10.tex ├── Lecture11.pdf ├── Lecture11.tex ├── Lecture12.Rmd ├── Lecture12.pdf ├── Lecture13.pdf ├── Lecture13.tex ├── Lecture2.Rmd ├── Lecture2.pdf ├── Lecture3.Rmd ├── Lecture3.pdf ├── Lecture4.docx ├── Lecture4.pdf ├── Lecture5.Rmd ├── Lecture5.pdf ├── Lecture6.Rmd ├── Lecture6.pdf ├── Lecture7.pdf ├── Lecture7.tex ├── Lecture8.pdf ├── Lecture8.tex ├── Lecture9.Rmd ├── Lecture9.pdf ├── Lecture_Notes.Rmd └── Lecture_Notes.pdf ├── Paper ├── A Geometric Approach to Sample Compression.pdf ├── Appendix - The Expressive Power of Neural Networks- A View from the Width.pdf ├── Asynchronous Stochastic Gradient Descent with Delay Compensation.pdf ├── Boosting the Margin_A New Explanation for the Effectiveness of Voting Methods.pdf ├── Decoupled Neural Interfaces using Synthetic Gradients.pdf ├── Deformable Convolutional Networks.pdf ├── Distributed Representations of Words and Phrases and their Compositionality.pdf ├── Dynamic Routing Between Capsules.pdf ├── Efficient Estimation of Word Representations in Vector Space.pdf ├── Generalization Bounds of SGLD for Non-convex Learning- Two Theoretical Viewpoints.pdf ├── Human-level control through deep reinforcement learning.pdf ├── Matrix Capsules with Em Routing.pdf ├── On the Complexity of Teaching.pdf ├── On the Recursive Teaching Dimension of VC Classes.pdf ├── Open Problem_Recursive Teaching Dimension Versus VC Dimension.pdf ├── Opening the black box of Deep Neural Networks via Information.pdf ├── Order Compression Schemes.pdf ├── Q-Learning.pdf ├── Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes.pdf ├── Recursive Teaching Dimension, VC-Dimension and Sample Compression.pdf ├── Simplified PAC-Bayesian Margin Bounds.pdf ├── Sobolev Training for Neural Networks.pdf ├── Stability and Generalization.pdf ├── Teaching and Compressing for Low VC-Dimension.pdf ├── The Expressive Power of Neural Networks- A View from the Width.pdf ├── The Multiplicative Weights Update Method- A Meta-Algorithm and Applications.pdf ├── The Strength of Weak Learnability.pdf ├── The Weighted Majority Algorithm.pdf ├── Understanding Synthetic Gradients and Decoupled Neural Interfaces.pdf ├── Value Iteration.pdf └── 周志华机器学习_第16章强化学习.pdf ├── README.md └── Term Project ├── CapsNet ├── .gitignore ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── capsLayer.py ├── capsNet.py ├── config.py ├── dist_version │ ├── README.md │ ├── capsnet_slim.py │ └── distributed_train.py ├── download_data.py ├── imgs │ ├── capsuleVSneuron.png │ └── my_wechat_QR.png ├── main.py ├── plot_acc.R ├── results │ ├── accuracy_1_iter.csv │ ├── accuracy_2_iter.csv │ ├── accuracy_3_iter.csv │ ├── margin_loss.png │ ├── reconstruction_loss.png │ ├── routing_trials.png │ └── total_loss.png └── utils.py └── project ├── jmlr2e.sty ├── jmlr2e.sty.bak ├── sample.aux ├── sample.bbl ├── 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