This paper deals with the formal question of machine reading. It proposes a novel methodology for automatic dataset building for machine reading model evaluation. To do so, the authors leverage on news resources that are equipped with a summary to generate a large number of questions about articles by replacing the named entities of it. Furthermore a attention enhanced LSTM inspired reading model is proposed and evaluated. The paper is well-written and clear, the originality seems to lie on two aspects. First, an original methodology of question answering dataset creation, where context-query-answer triples are automatically extracted from news feeds. Such proposition can be considered as important because it opens the way for large model learning and evaluation. The second contribution is the addition of an attention mechanism to an LSTM reading model. the empirical results seem to show relevant improvement with respect to an up-to-date list of machine reading models.

Given the lack of an appropriate dataset, the author provides a new dataset which scraped CNN and Daily Mail, using both the full text and abstract summaries/bullet points. The dataset was then anonymised (i.e. entity names removed). Next the author presents a two novel Deep long-short term memory models which perform well on the Cloze query task.

We want to find two matrices $W$ and $H$ such that $V = WH$. Often a goal is to determine underlying patterns in the relationships between the concepts represented by each row and column. $W$ is some $m$ by $n$ matrix and we want the inner dimension of the factorization to be $r$. So 

$$\underbrace{V}_{m \times n} = \underbrace{W}_{m \times r} \underbrace{H}_{r \times n}$$

Let's consider an example matrix where of three customers (as rows) are associated with three movies (the columns) by a rating value.

$$
V = \left[\begin{array}{c c c}
5 & 4 & 1  \\\\
4 & 5 & 1 \\\\
2 & 1 & 5
\end{array}\right]
$$


We can decompose this into two matrices with $r = 1$. First lets do this without any non-negative constraint using an SVD reshaping matrices based on removing eigenvalues:


$$
W = \left[\begin{array}{c c c}
-0.656 \\\
 -0.652 \\\
 -0.379
\end{array}\right],
H = \left[\begin{array}{c c c}
-6.48 & -6.26 & -3.20\\\\
\end{array}\right]
$$

We can also decompose this into two matrices with $r = 1$ subject to the constraint that $w_{ij} \ge 0$ and  $h_{ij} \ge 0$. (Note: this is only possible when $v_{ij} \ge 0$):

$$
W = \left[\begin{array}{c c c}
0.388 \\\\
0.386 \\\\
0.224
\end{array}\right],
H = \left[\begin{array}{c c c}
11.22 & 10.57 & 5.41  \\\\
\end{array}\right]
$$

Both of these $r=1$ factorizations reconstruct matrix $V$ with the same error. 

$$
V \approx WH = \left[\begin{array}{c c c}
4.36 & 4.11 & 2.10 \\\
4.33 & 4.08 & 2.09 \\\
2.52 & 2.37 & 1.21 \\\
\end{array}\right]
$$


If they both yield the same reconstruction error then why is a non-negativity constraint useful? We can see above that it is easy to observe patterns in both factorizations such as similar customers and similar movies. `TODO: motivate why NMF is better`



#### Paper Contribution 

This paper discusses two approaches for iteratively creating a non-negative $W$ and $H$ based on random initial matrices. The paper discusses a multiplicative update rule where the elements of $W$ and $H$ are iteratively transformed by scaling each value such that error is not increased. 

The multiplicative approach is discussed in contrast to an additive gradient decent based approach where small corrections are iteratively applied. The multiplicative approach can be reduced to this by setting the learning rate ($\eta$) to a ratio that represents the magnitude of the element in $H$ to the scaling factor of $W$ on $H$.



### Still a draft

Deeper networks should never have a higher **training** error than smaller ones. In the worst case, the layers should "simply" learn identities. It seems as this is not so easy with conventional networks, as they get much worse with more layers. So the idea is to add identity functions which skip some layers. The network only has to learn the **residuals**. 

Advantages:

* Learning the identity becomes learning 0 which is simpler
* Loss in information flow in the forward pass is not a problem anymore
    * No vanishing / exploding gradient
* Identities don't have parameters to be learned

## Evaluation

The learning rate starts at 0.1 and is divided by 10 when the error plateaus. Weight decay of 0.0001 ($10^{-4}$), momentum of 0.9. They use mini-batches of size 128.

* ImageNet ILSVRC 2015: 3.57% (ensemble)
* CIFAR-10: 6.43%
* MS COCO: 59.0% mAp@0.5 (ensemble)
* PASCAL VOC 2007: 85.6% mAp@0.5
* PASCAL VOC 2012: 83.8% mAp@0.5

## See also

* [DenseNets](http://www.shortscience.org/paper?bibtexKey=journals/corr/1608.06993)

Everyone has been thinking about how to apply GANs to discrete sequence data for the past year or so. This paper presents the model that I would guess most people thought of as the first-thing-to-try:

1. Build a recurrent generator model which samples from its softmax outputs at each timestep.
2. Pass sampled sequences to a recurrent discriminator model which distinguishes between sampled sequences and real-data sequences.
3. Train the discriminator under the standard GAN loss.
4. Train the generator with a REINFORCE (policy gradient) objective, where each trajectory is assigned a single episodic reward: the score assigned to the generated sequence by the discriminator.

Sounds hacky, right? We're learning a generator with a high-variance model-free reinforcement learning algorithm, in a very seriously non-stationary environment. (Here the "environment" is a discriminator being jointly learned with the generator.)

There's just one trick in this paper on top of that setup: for non-terminal states, the reward is defined as the *expectation* of the discriminator score after stochastically generating from that state forward. To restate using standard (somewhat sloppy) RL syntax, in different terms than the paper: (under stochastic sequential policy $\pi$, with current state $s_t$, trajectory $\tau_{1:T}$ and discriminator $D(\tau)$)

$$r_t = \mathbb E_{\tau_{t+1:T} \sim \pi(s_t)} \left[ D(\tau_{1:T}) \right]$$

The rewards are estimated via Monte Carlo — i.e., just take the mean of $N$ rollouts from each intermediate state. They claim this helps to reduce variance. That makes intuitive sense, but I don't see any results in the paper demonstrating the effect of varying $N$.

---

Yep, so it turns out that this sort of works.. with a big caveat:

## The big caveat

Graph from appendix:

![](https://www.dropbox.com/s/5fqh6my63sgv5y4/Bildschirmfoto%202016-09-27%20um%2021.34.44.png?raw=1)

SeqGANs don't work without supervised pretraining. Makes sense — with a cold start, the generator just samples a bunch of nonsense and the discriminator overfits. Both the generator and discriminator are pretrained on supervised data in this paper (see Algorithm 1).

I think it must be possible to overcome this with the proper training tricks and enough sweat. But it's probably more worth our time to address the fundamental problem here of developing better RL for structured prediction tasks.

The main contribution of this paper is introducing a new transformation that the authors call Batch Normalization (BN). The need for BN comes from the fact that during the training of deep neural networks (DNNs) the distribution of each layer’s input change. This phenomenon is called internal covariate shift (ICS).

#### What is BN?
Normalize each (scalar) feature independently with respect to the mean and variance of the mini batch. Scale and shift the normalized values with two new parameters (per activation) that will be learned. The BN consists of making normalization part of the model architecture.

#### What do we gain?
According to the author, the use of BN provides a great speed up in the training of DNNs. In particular, the gains are greater when it is combined with higher learning rates. In addition, BN works as a regularizer for the model which allows to use less dropout or less L2 normalization. Furthermore, since the distribution of the inputs is normalized, it also allows to use sigmoids as activation functions without the saturation problem.

#### What follows?
This seems to be specially promising for training recurrent neural networks (RNNs). The vanishing and exploding gradient problems \cite{journals/tnn/BengioSF94} have their origin in the iteration of transformation that scale up or down the activations in certain directions (eigenvectors). It seems that this regularization would be specially useful in this context since this would allow the gradient to flow more easily. When we unroll the RNNs, we usually have ultra deep networks.

#### Like
* Simple idea that seems to improve training.
* Makes training faster.
* Simple to implement. Probably.
* You can be less careful with initialization.

#### Dislike
* Does not work with stochastic gradient descent (minibatch size = 1).
* This could reduce the parallelism of the algorithm since now all the examples in a mini batch are tied.
* Results on ensemble of networks for ImageNet makes it harder to evaluate the relevance of BN by itself. (Although they do mention the performance of a single model).