The goal of this paper is to apply this background, particularly to "identify fallacies made by dialogue participants" on the WWW. The Semantic Web is seen as a way to do this.

This paper begins by presenting a definition of argumentation, reviewing Walton's and Toulmin's analyses of arguments, and showing basic argument diagrams. It also discusses the importance, for computer modelling, of classifications of arguments, citing Walton's 1996 book, Argumentation Schemes for Presumptive Reasoning, as the most influential of these. Then, it presents the notion of critical questions and criteria for argument acceptability, which form part of the core basis for this work. Finally, dialogue games are mentioned in passing.

_Objective:_ Improve GANs convergence to more diverse and visually pleasing images at higher resolution using a novel equilibrium method between the discriminator and the generator that also simplifies training procedures.

_Dataset:_ [LFW](http://vis-www.cs.umass.edu/lfw/)

## Inner workings:

They try to match the distribution of the errors (assumed to be normally distributed) instead of matching the distribution of the samples directly. In order to do this they compute the Wasserstein distance between a pixel-wise autoencoder loss distributions of real and generated samples defined as follow:

1.  Autoencoder loss:

[![screen shot 2017-04-24 at 3 46 32 pm](https://cloud.githubusercontent.com/assets/17261080/25340190/429f9788-2905-11e7-88dc-b44567b9cd34.png)](https://cloud.githubusercontent.com/assets/17261080/25340190/429f9788-2905-11e7-88dc-b44567b9cd34.png)

2.  Wasserstein distance for two normal distributions μ1 = N(m1, C1) and μ2 = N(m2, C2)

[![screen shot 2017-04-24 at 3 46 44 pm](https://cloud.githubusercontent.com/assets/17261080/25340191/42b23474-2905-11e7-9810-58d5326bf886.png)](https://cloud.githubusercontent.com/assets/17261080/25340191/42b23474-2905-11e7-9810-58d5326bf886.png)

They also introduce an equilibrium concept to account for the situation when `G` and `D` are not well balanced and the discriminator `D` wins easily. This is controlled by what they call the diversity ratio that balances between auto-encoding real images and discriminating real from generated images. It is defined as follow:  
[![screen shot 2017-04-24 at 3 56 29 pm](https://cloud.githubusercontent.com/assets/17261080/25340609/992c2188-2906-11e7-8c51-498bbd293119.png)](https://cloud.githubusercontent.com/assets/17261080/25340609/992c2188-2906-11e7-8c51-498bbd293119.png)

To maintain this balance they use a standard SGD but they introduce a variable `kt` initially 0 to control how much emphasis is put on the generator `G`. This removes the need to do `x` steps on `D` followed by `y` steps on `G` or to pretrained one of the two.  
[![screen shot 2017-04-24 at 3 59 57 pm](https://cloud.githubusercontent.com/assets/17261080/25340859/4ee06476-2907-11e7-971f-90421449cb51.png)](https://cloud.githubusercontent.com/assets/17261080/25340859/4ee06476-2907-11e7-971f-90421449cb51.png)

Finally they derive a global convergence measure by using the equilibrium concept that can be used to determine when the network has reached its final state or if the model has collapsed:  
[![screen shot 2017-04-24 at 4 04 12 pm](https://cloud.githubusercontent.com/assets/17261080/25340998/b8bf6ad6-2907-11e7-8afa-294cae32c6af.png)](https://cloud.githubusercontent.com/assets/17261080/25340998/b8bf6ad6-2907-11e7-8afa-294cae32c6af.png)

## Architecture:

They tried to keep the architecture simple to really study the impact of their new equilibrium principle and loss. They don't use batch normalization, dropout, transpose convolutions or exponential growth for convolution filters.

[![screen shot 2017-04-24 at 4 09 29 pm](https://cloud.githubusercontent.com/assets/17261080/25341219/6fb7be28-2908-11e7-8774-287c1b7d7684.png)](https://cloud.githubusercontent.com/assets/17261080/25341219/6fb7be28-2908-11e7-8774-287c1b7d7684.png)

## Results:

They trained on images from 32x32 to 256x256, but at higher resolution images tend to lose sharpness. Nevertheless images are very very good!  
[![screen shot 2017-04-24 at 4 20 30 pm](https://cloud.githubusercontent.com/assets/17261080/25341699/f99b0770-2909-11e7-84a0-3ac0436771e5.png)](https://cloud.githubusercontent.com/assets/17261080/25341699/f99b0770-2909-11e7-84a0-3ac0436771e5.png)

(Reposting under ACL 2017 version)

Kind of a response/deeper dive into the durret/klein "easy victories" paper. Suggests that a) lexical features they used ("easy victories") are very prone to overfitting. They first show that several state of the art systems that use lexical features, trained on CoNLL data, perform poorly on wikiref, which was annotated using the same guidelines. Meanwhile the stanford sieve system performs about the same on both.
Then they show that a high percentage of gold standard linked headwords in the test set have been seen in the training set, and that a much lower percentage of errors are in the training set, implying that lexical features just allow you to memorize what kinds of things can be linked.
They suggest development of robust features, including using embeddings as lexical features, using lexical representations only for context, and on the evaluation side, using test sets that are different domains than the training set.

They represent an image as a tree where leafs are pixels and nodes represent clusters of those pixels. They train by regressing for some possible segmented region $r$ on the following function for every segmentation example and ground truth:
$$S(r)=\frac{\\#(g) - \\#(r)}{\max(\\#(r), \\#(g)))}$$

Here $\\#(g)$ is the number of pixels in the ground truth and $\\#(r)$ is the number of pixels in the example segmentation. What is not explained here is what other information is used because it cannot simple be pixel counts. This function is used to rank the nodes in every path from the root to the leafs in Figure (a). 

The idea for the segmentation is that there is some set of nodes such that you can draw a line shown in Figure (b) which is equivalent to selecting a segmentation. The paper goes on to compute this using a dynamic programming solution based on the fact that the same pixel segmentations will be considered multiple times.

![](http://i.imgur.com/FEky9dK.png)

I think the idea is great but the initial idea for the regression is unclear.

This paper examines the problem of approximate graph matching (isomorphism). Given graphs G, H with p nodes, represented by respective adjacency matrices A, B, Find a permutation matrix P that best "matches" AP and PB. 

This paper poses the multimodal graph matching problem as a convex optimization problem, and solves it using augmented Langrangian techniques (viz., ADMM). This is an important problem with application in several fields. Experimental results on synthetic and multiple real world datasets demonstrate effectiveness of the proposed approach.