#### Introduction

* Introduces a new global log-bilinear regression model which combines the benefits of both global matrix factorization and local context window methods.


#### Global Matrix Factorization Methods

* Decompose large matrices into low-rank approximations.
* eg - Latent Semantic Analysis (LSA)

##### Limitations

* Poor performance on word analogy task
* Frequent words contribute disproportionately high to the similarity measure.

#### Shallow, Local Context-Based Window Methods

* Learn word representations using adjacent words.
* eg - Continous bag-of-words (CBOW) model and skip-gram model.

##### Limitations

* Since they do not operate directly on the global co-occurrence counts, they can not utilise the statistics of the corpus effectively.


#### GloVe Model

* To capture the relationship between words $i$ and $j$, word vector models should use ratios of co-occurene probabilites (with other words $k$) instead of using raw probabilites themselves.
* In most general form:
     * $F(w_{i}, w_{j}, w_{k}^{~} ) = P_{ik}/P_{jk}$
* We want $F$ to encode information in the vector space (which have a linear structure), so we can restrict to the difference of $w_{i}$ and $w_{j}$
    * $F(w_{i} - w_{j}, w_{k}^{~} ) = P_{ik}/P_{jk}$
* Since right hand side is a scalar and left hand side is a vector, we take dot product of the arguments.
    * $F( (w_{i} - w_{j})^{T}, w_{k}^{~} ) = P_{ik}/P_{jk}$
* *F* should be invariant to order of the word pair $i$ and $j$.
    * $F(w_{i}^{T}w_{k}^{~}) = P_{ik}$
* Doing further simplifications and optimisations (refer paper), we get cost function, 
    * $J = \sum_{\text{over all i, j pairs in the vocabulary}}[w_{i}^{T}w_{k}^{~} + b_{i} + b_{k}^{~} - log(X_{ik})]^{2}$
    * $f$ is a weighing function.
    * $f(x) = min((x/x_{max})^{\alpha}, 1)$
    * Typical values, $x_{\max} = 100$ and $\alpha = 3/4$
    * *b* are the bias terms.

##### Complexity

* Depends on a number of non-zero elements in the input matrix.
* Upper bound by the square of vocabulary size
* Since for shallow window-based approaches, complexity depends on $|C|$ (size of the corpus), tighter bounds are needed.
* By modelling number of co-occurrences of words as power law function of frequency rank, the complexity can be shown to be proportional to $|C|^{0.8}$

#### Evaluation

##### Tasks

* Word Analogies
    * a is to b as c is to ___?
    * Both semantic and syntactic pairs
    * Find closest d to $w_{b} - w_{c} + w_{a}$ (using cosine similarity)

* Word Similarity
* Named Entity Recognition

##### Datasets

* Wikipedia Dumps - 2010 and 2014
* Gigaword5
* Combination of Gigaword5 and Wikipedia2014
* CommonCrawl
* 400,000 most frequent words considered from the corpus.

##### Hyperparameters

* Size of context window.
* Whether to distinguish left context from right context.
* $f$ - Word pairs that are $d$ words apart contribute $1/d$ to the total count.
* $xmax = 100$
* $\alpha = 3/4$
* AdaGrad update

##### Models Compared With

* Singular Value Decomposition
* Continous Bag-Of-Words
* Skip-Gram

##### Results

* Glove outperforms all other models significantly.
* Diminishing returns for vectors larger than 200 dimensions.
* Small and asymmetric context windows (context window only to the left) works better for syntactic tasks.
* Long and symmetric context windows (context window to both the sides) works better for semantic tasks.
* Syntactic task benefited from larger corpus though semantic task performed better with Wikipedia instead of Gigaword5 probably due to the comprehensiveness of Wikipedia and slightly outdated nature of Gigaword5.
* Word2vec’s performance decreases if the number of negative samples increases beyond about 10.
* For the same corpus, vocabulary, and window size GloVe consistently achieves better results, faster.

This paper models object detection as a regression problem for bounding
boxes and object class probabilities with a single pass through the CNN. The
main contribution is the idea of dividing the image into a 7x7 grid, and having
each cell predict a distribution over class labels as well as a bounding box
for the object whose center falls into it. It's much faster than R-CNN and
Fast R-CNN, as the additional step of extracting region proposals has been
removed.

## Strengths

- Works real-time. Base model runs at 45fps and a faster version goes up to
150fps, and they claim that it's more than twice as fast as other works on
real-time detection.

- End-to-end model; Localization and classification errors can be jointly
optimized.

- YOLO makes more localization errors and fewer background mistakes than
Fast R-CNN, so using YOLO to eliminate false background detections from
Fast R-CNN results in ~3% mAP gain (without much computational time as R-CNN
is much slower).

## Weaknesses / Notes

- Results fall short of state-of-the-art: 57.9% v/s 70.4% mAP (Faster R-CNN).

- Performs worse at detecting small objects, as at most one object per grid
cell can be detected.

The paper introduces a sequential variational auto-encoder that generates complex images iteratively. The authors also introduce a new spatial attention mechanism that allows the model to focus on small subsets of the image. This new approach for image generation produces images that can’t be distinguished from the training data.

#### What is DRAW:
The deep recurrent attention writer (DRAW) model has two differences with respect to other variational auto-encoders. First, the encoder and the decoder are recurrent networks. Second, it includes an attention mechanism that restricts the input region observed by the encoder and the output region observed by the decoder.

#### What do we gain?
The resulting images are greatly improved by allowing a conditional and sequential generation. In addition, the spatial attention mechanism can be used in other contexts to solve the “Where to look?” problem.

#### What follows?
A possible extension to this model would be to use a convolutional architecture in the encoder or the decoder. Although this might be less useful since we are already restricting the input of the network.

#### Like:
* As observed in the samples generated by the model, the attention mechanism works effectively by reconstructing images in a local way.
* The attention model is fully differentiable.

#### Dislike:
* I think a better exposition of the attention mechanism would improve this paper.

Hosseini and Poovendran propose semantic adversarial examples by randomly manipulating hue and saturation of images. In particular, in an iterative algorithm, hue and saturation are randomly perturbed and projected back to their valid range. If this results in mis-classification the perturbed image is returned as the adversarial example and the algorithm is finished; if not, another iteration is run. The result is shown in Figure 1. As can be seen, the structure of the images is retained while hue and saturation changes, resulting in mis-classified images.

https://i.imgur.com/kFcmlE3.jpg
Figure 1: Examples of the computed semantic adversarial examples.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

This is an interestingly pragmatic paper that makes a super simple observation. Often, we may want a usable network with fewer parameters, to make our network more easily usable on small devices. It's been observed (by these same authors, in fact), that pruned networks can achieve comparable weights to their fully trained counterparts if you rewind and retrain from early in the training process, to compensate for the loss of the (not ultimately important) pruned weights. This observation has been dubbed the "Lottery Ticket Hypothesis", after the idea that there's some small effective subnetwork you can find if you sample enough networks. 

Given these two facts - the usefulness of pruning, and the success of weight rewinding - the authors explore the effectiveness of various ways to train after pruning. Current standard practice is to prune low-magnitude weights, and then continue training remaining weights from values they had at pruning time, keeping the final learning rate of the network constant. The authors find that: 
1. Weight rewinding, where you rewind weights to *near* their starting value, and then retrain using the learning rates of early in training, outperforms fine tuning from the place weights were when you pruned 

but, also 
2. Learning rate rewinding, where you keep weights as they are, but rewind learning rates to what they were early in training, are actually the most effective for a given amount of training time/search cost 

To me, this feels a little bit like burying the lede: the takeaway seems to be that when you prune, it's beneficial to make your network more "elastic" (in the metaphor-to-neuroscience sense) so it can more effectively learn to compensate for the removed neurons. So, what was really valuable in weight rewinding was the ability to "heat up" learning on a smaller set of weights, so they could adapt more quickly. And the fact that learning rate rewinding works better than weight rewinding suggests that there is value in the learned weights after all, that value is just outstripped by the benefit of rolling back to old learning rates. 

All in all, not a super radical conclusion, but a useful and practical one to have so clearly laid out in a paper.