This paper is overly mathematical for what it accomplished (117 equations) but is somewhat interesting.  Cover considers CRPs (constantly rebalanced portfolios), where a certain portion of wealth is "fixed" in each stock and the portfolio is rebalanced every period: CRPs can earn positive return even if none of the stock's do. Finding the best CRP represents an optimization over a simplex (since we optimize over the fraction to invest in each stock), while the best stock is just a corner of the simplex, so the best CRP does at least as well as the best stock.  He considers the strategy of initially investing equal money in each CRP (i.e. over the continuous space of CRPs) and then letting the wealth in each CRP grow: doing this, he derives a bound that the terminal wealth of this strategy will only lag the terminal wealth of the best CRP chosen in hindsight by a factor  $(T+1)^{(N-1)}$, when there are $T$ periods and $N$ assets.  Thus the strategy earns the same asymptotic growth as the best CRP.  In practice I found that this technique isn't useful and the bound is very very weak (even over 50 years of data).  However, the idea of buy-and-hold having good asymptotic performance and comparing well to the best portfolio chosen in hindsight is a good one.

_Objective:_ Build a network easily trainable by back-propagation to perform unsupervised domain adaptation while at the same time learning a good embedding for both source and target domains.

 _Dataset:_ [SVHN](ufldl.stanford.edu/housenumbers/), [MNIST](yann.lecun.com/exdb/mnist/), [USPS](https://www.otexts.org/1577), [CIFAR](https://www.cs.toronto.edu/%7Ekriz/cifar.html) and [STL](https://cs.stanford.edu/%7Eacoates/stl10/).

#### Architecture:

Very similar to RevGrad but with some differences.

Basically a shared encoder and then a classifier and a reconstructor.  
[![screen shot 2017-05-22 at 6 11 22 pm](https://cloud.githubusercontent.com/assets/17261080/26318076/21361592-3f1a-11e7-9213-9cc07cfe2f2a.png)](https://cloud.githubusercontent.com/assets/17261080/26318076/21361592-3f1a-11e7-9213-9cc07cfe2f2a.png)

The two losses are:

*   the usual cross-entropy with softmax for the classifier
*   the pixel-wise squared loss for reconstruction

Which are then combined using a trade-off hyper-parameter between classification and reconstruction.

They also use data augmentation to generate additional training data during the supervised training using only geometrical deformation: translation, rotation, skewing, and scaling

Plus denoising to reconstruct clean inputs given their noisy counterparts (zero-masked noise and Gaussian noise).

#### Results:

Outperforms state of the art on most tasks at the time, now outperformed itself by Generate To Adapt on most tasks.

This paper is about the analysis of CNNs. It seems to be extremely similar to what Zeiler & Fergus did. I can't see the contribution. Only cited 7 times, although it is from December 2014 -> I suggest to read the Zeiler & Fergus paper instead.

## Related

* 2013, Zeiler & Fergus: [Visualizing and Understanding Convolutional Networks ](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13#martinthoma)


## Errors

* " Section ?? provides"

Papernot et al. build upon the idea of network distillation [1] and propose a simple mechanism to defend networks against adversarial attacks. The main idea of distillation – originally introduced to “distill” the knowledge of very deep networks into smaller ones – is to train a second, possibly smaller network, with the probability distributions of the original, possibly larger network as supervision. Papernot et al. as well as the authors of [1] argue that the probability distributions, i.e. the activations of the final softmax layer (also referred to as “soft” labels), contain rich information about the task in contrast to the true “hard” labels. This allows the network to achieve similar performance while using less parameters or a different architecture.

However, Papernot et al. do not distill a network's knowledge into a smaller one; instead they use distillation to make networks robust against adversarial attacks. They argue that most algorithms to generate adversarial examples make use of the “adversarial gradient”; i.e. the gradient of the network's cost w.r.t. its input. The adversarial gradient then guides perturbation of the input image in the direction of wrong classes (the authors consider a simple classification task for simplicity). Therefore, Papernot et al. Argure, the gradient around training samples needs to be reduced – in other words, the model needs to be smoothed.

https://i.imgur.com/jXIhIGz.png

The proposed approach is very simple, they just distill the knowledge of the network into another network with same architectures and hyper parameters. By using the probability distributions as “soft” labels instead of the hard labels for training, the network is essentially smoothed. The full procedure is illustrated in Figure 1.

Despite the simplicity of the approach, I want to highlight some additional key observations:
- Distillation is also supposed to help generalization by avoiding overly confident networks.
- The success rate of adversarial attacks can be reduced significantly as shown in quantitative experiments.
- The amplitude of adversarial gradients can be reduced, which means that the network has been smoothed and is less sensitive to variations in the input samples.

Also see this summary on [davidstutz.de](https://davidstutz.de/category/reading/).

TLDR; The author present and end-2-end dialog system that consists of an LSTM, action templates, an entity extraction system, and custom code for declaring business rules. They test the systme on a toy task where the goal is to call a person from an address book. They train the system on 21 dialogs using Supervised Learning, and then optimize it using Reinforcement Learning, achieving 70% task completion rates.

#### Key Points

- Task: User asks to call person. Action: Find in address book and place call
- 21 example dialogs
- Several hundred lines of Python code to block certain actions
- External entity recognition API
- Hand-crafted features as input to the LSTM. Hand-crafted action template.
- RNN maps from sequence to action template, First pre-train LSTM to reproduce dialogs using Supervised Learning, then train using RL / policy gradient
- The system doesn't generate text, it picks a template


#### Notes

- I wonder how well the system would generalize to a task that has a larger action space and more varied conversations. The 21 provided dialogs cover a lot of the taks space already. Much harder to do that in larger spaces.
- I wouldn't call this approach end-to-end ;)