This combines the ideas of recurrent attention to perform object detection in an image \cite{1406.6247} for multiple objects \cite{1412.7755} with semantic segmentation \cite{1505.04366}. 

Segmenting subregions is to avoid a global resolution bias (the object would take up the majority of pixels) and to allow multiple scales of objects to be segmented. 

Here is a video that demos the method described in the paper:

https://youtu.be/BMVDhTjEfBU

## Introduction

* Introduces techniques to learn word vectors from large text datasets.
* Can be used to find similar words (semantically, syntactically, etc).
* [Link to the paper](http://arxiv.org/pdf/1301.3781.pdf)
* [Link to open source implementation](https://code.google.com/archive/p/word2vec/)

## Model Architecture

* Computational complexity defined in terms of a number of parameters accessed during model training.
* Proportional to $E*T*Q$
* *E* - Number of training epochs
* *T* - Number of words in training set
* *Q* - depends on the model

### Feedforward Neural Net Language Model (NNLM)

* Probabilistic model with input, projection, hidden and output layer.
* Input layer encodes N previous word using 1-of-V encoding (V is vocabulary size).
* Input layer projected to projection layer P with dimensionality *N\*D*
* Hidden layer (of size *H*) computes the probability distribution over all words.
* Complexity per training example $Q =N*D + N*D*H + H*V$
* Can reduce *Q* by using hierarchical softmax and Huffman binary tree (for storing vocabulary).

### Recurrent Neural Net Language Model (RNNLM)

* Similar to NNLM minus the projection layer.
* Complexity per training example $Q =H*H + H*V$
* Hierarchical softmax and Huffman tree can be used here as well.

## Log-Linear Models

* Nonlinear hidden layer causes most of the complexity.
* NNLMs can be successfully trained in two steps:
  *  Learn continuous word vectors using simple models.
  *  N-gram NNLM trained over the word vectors.


### Continuous Bag-of-Words Model

* Similar to feedforward NNLM.
* No nonlinear hidden layer.
* Projection layer shared for all words and order of words does not influence projection.
* Log-linear classifier uses a window of words to predict the middle word.
* $Q = N*D + D*\log_2V$

### Continuous Skip-gram Model

* Similar to Continuous Bag-of-Words but uses the middle world of the window to predict the remaining words in the window.
* Distant words are given less weight by sampling fewer distant words.
* $Q = C*(D + D*log_2 V$) where *C* is the max distance of the word from the middle word.
* Given a *C* and a training data, a random *R* is chosen in range *1 to C*.
* For each training word, *R* words from history (previous words) and *R* words from future (next words) are marked as target output and model is trained.


## Results

* Skip-gram beats all other models for semantic accuracy tasks (eg - relating Athens with Greece).
* Continuous Bag-of-Words Model outperforms other models for semantic accuracy tasks (eg great with greater) - with skip-gram just behind in performance.
* Skip-gram architecture combined with RNNLMs outperforms RNNLMs (and other models) for Microsoft Research Sentence Completion Challenge.
* Model can learn relationships like "Queen is to King as Woman is to Man". This allows algebraic operations like Vector("King") - Vector("Man") + Vector("Woman").

This paper proposes a conditional GAN-based image captioning model.
Given an image, the generator generates a caption, and given an image
and caption, the discriminator/evaluator distinguishes between generated
and real captions. Key ideas:

- Since caption generation involves sequential sampling, which is
non-differentiable, the model is trained with policy gradients, with
the action being the choice of word at every time step, policy being
the distribution over words, and reward the score assigned by the
evaluator to generated caption.

- The evaluator's role assumes a completely generated caption as input
(along with image), which in practice leads to convergence issues. Thus
to accommodate feedback for partial sequences during training, Monte Carlo
rollouts are used, i.e. given a partial generated sequence, n completions
are sampled and run through the evaluator to compute reward.

- The evaluator's objective function consists of three terms
    - image-caption pairs from training data (positive)
    - image and generated captions (negative)
    - image and sampled captions for other images from training data (negative)

- Both the generator and evaluator are pretrained with supervision / MLE, then
fine-tuned with policy gradients. During inference, evaluator score is used as
the beam search objective.

## Strengths

This is neat paper with insightful ideas (Monte Carlo rollouts for assigning
rewards to partial sequences, evaluator score as beam search objective),
and is perhaps the first work on C-GAN-based image captioning.

## Weaknesses / Notes

### Contribution

The author conducts five experiments on EC2 to assess the impact of software-defined virtual networking with HTTP on composite container applications. Compared to previous container performance studies, it contributes new insight into the overlay networking aspect specifically for VM-hosted containers. Evidently, the SDVN causes a major performance loss whereas the container itself as well as the encryption cause minor (but still not negligible) losses. The results indicate that further practical work on container networking tools and stacks is needed for performance-critical distributed applications.

### Strong points

The methodology of measuring the performance and using a baseline performance result is appropriate. The author provides the benchmark tooling (ppbench) and reference results (in dockerised form) to enable recomputable research.

### Weak points

The title mentions microservices and the abstract promises design recommendations for microservice architectures. Yet, the paper only discusses containers which are a potential implementation technology but neither necessary for nor guaranteed to be microservices. Reducing the paper scope to just containers would be fair. The introduction contains an unnecessary redundant mention of Kubernetes, CoreOS, Mesos and reference [9] around the column wrap. The notation of SDN vs. SDVN is inconsistent between text and images; due to SDN being a wide area of research, the consistent use of SDVN is recommended. Fig. 3b is not clearly labelled. Resulting transfer losses - 100% means no loss, this is confusing. The y axis should presumably be inverted so that losses show highest for SDN with about 70%. The performance breakdown around 300kB messages in Fig. 2 is not sufficiently explained. Is it a repeating phenomenon which might be related to packet scheduling? The "just Docker" networking configuration is not explained, does it run in host or bridge mode? Which version of Docker was used? The size and time distribution of the 6 million HTTP requests should also be explained in greater detail to see how much randomness was involved.

### Further comments

The work assumes that containers are always hosted in virtual machines while bare metal container hosting in the form of CaaS becomes increasingly available (Triton, CoreOS OnMetal, etc.). The results by Felter et al. are mentioned but not put into perspective. A comparison of how the networking is affected by VM/BM hosting would be a welcome addition, although AWS would probably not be a likely environment due to ECS running atop EC2.

TLDR; The authors propose an importance-sampling approach to deal with large vocabularies in NMT models. During training, the corpus is partitioned, and for each partition only target words occurring in that partition are chosen. To improve decoding speed over the full vocabulary, the authors build a dictionary mapping from source sentence to potential target vocabulary. The authors evaluate their approach on standard MT tasks and perform better than the baseline models with smaller vocabulary.

#### Key Points:

- Computing partition function is the bottleneck. Use sampling-based approach.
- Dealing with large vocabulary during training is separate from dealing with large vocab during decoding. Training is handled with importance sampling. Decoding is handled with source-based candidate list.
- Decoding with candidate list takes around 0.12s (0.05) per token on CPU (GPU). Without target list 0.8s (0.25s).
- Issue: Candidate list is depended on source sentence, so it must be re-computed for each sentence. 
- Reshuffling the data set is expensive as new partitions need to be calculated (not necessary, but improved scores).

#### Notes:

- How is the corpus partitioned? What's the effect of the partitioning strategy?
- The authors say that they replace UNK tokens using "another word alignment model" but don't go into detail what this is. The results show that doing this results in much larger score bump than increasing the vocab does. (The authors do this for all comparison models though).
- Reshuffling the dataset also results in a significant performance bump, but this operation is expensive. IMO the authors should take all these into account when reporting performance numbers. A single training update may be a lot faster, but the setup time increases. I'd would've like to see the authors assign a global time budget to train/test and then compare the models based on that.
- The authors only briefly mentioned that re-building the target vocab for each source sentence is an issue and how they solve it, no details given.