Szegedy et al. were (to the best of my knowledge) the first to describe the phenomen of adversarial examples as researched today. Specifically, they described the main objective in order to obtain adversarial examples as

$\arg\min_r \|r\|_2$ s.t. $f(x+r)=l$ and $x+r$ being a valid image

where $f$ is the neural network and $l$ the target class (i.e. targeted adversarial example). In the paper, they originally headlined the section by “blind spots in neural networks”. While they give some explanation and provide experiments, also introducing the notion of transferability of adversarial examples and an idea of adversarial examples used as regularization during training, many questions are left open. The given conclusion, that these adversarial examples are highly unlikely and that these examples lie dense within regular training examples are controversial in the literature.

This article makes the argument for *interactive* language learning, motivated by some nice recent small-domain success. I can certainly agree with the motivation: if language is used in conversation, shouldn't we be building models which know how to behave in conversation?

The authors develop a standard multi-agent communication paradigm, where two agents learn communicate in a single-round reference game. (No references to e.g. Kirby or any [ILM][1] work, which is in the same space.) Agent `A1` examines a referent `R` and "transmits" a one-hot utterance representation to `A2`, who must successfully identify `R` given the utterance. The one-round conversations are a success when `A2` picks the correct referent `R`. The two agents are jointly trained to maximize this success metric via REINFORCE (policy gradient).

**This is mathematically equivalent to [the NVIL model (Mnih and Gregor, 2014)][2]**, an autoencoder with "hard" latent codes which is likewise trained by policy gradient methods.

They perform a nice thorough evaluation on both successful and "cheating" models. This will serve as a useful reference point / starting point for people interested in interactive language acquisition.

The way clear is forward, I think: let's develop agents in more complex environments, interacting in multi-round conversations, with more complex / longer utterances.

[1]: http://cocosci.berkeley.edu/tom/papers/IteratedLearningEvolutionLanguage.pdf
[2]: https://arxiv.org/abs/1402.0030

#### Introduction

* Introduces fastText, a simple and highly efficient approach for text classification.
* At par with deep learning models in terms of accuracy though an order of magnitude faster in performance. 
* [Link to the paper](http://arxiv.org/abs/1607.01759v3)
* [Link to code](https://github.com/facebookresearch/fastText)

#### Architecture

* Built on top of linear models with a rank constraint and a fast loss approximation.
* Start with word representations that are averaged into text representation and feed them to a linear classifier.
* Think of text representation as a hidden state that can be shared among features and classes.
* Softmax layer to obtain a probability distribution over pre-defined classes.
* High computational complexity $O(kh)$, $k$ is the number of classes and $h$ is dimension of text representation.

##### Hierarchial Softmax

* Based on Huffman Coding Tree
* Used to reduce complexity to $O(hlog(k))$
* Top T results (from the tree) can be computed efficiently $O(logT)$ using a binary heap.

##### N-gram Features

* Instead of explicitly using word order, uses a bag of n-grams to maintain efficiency without losing on accuracy.
* Uses [hashing trick](https://arxiv.org/pdf/0902.2206.pdf) to maintain fast and memory efficient mapping of the n-grams.

#### Experiments

##### Sentiment Analysis

* fastText benefits by using bigrams.
* Outperforms [char-CNN](http://arxiv.org/abs/1502.01710v5) and [char-CRNN](http://arxiv.org/abs/1602.00367v1) and performs a bit worse than [VDCNN](http://arxiv.org/abs/1606.01781v1).
* Order of magnitudes faster in terms of training time.
* Note: fastText does not use pre-trained word embeddings.

##### Tag Prediction

* fastText with bigrams outperforms [Tagspace](http://emnlp2014.org/papers/pdf/EMNLP2014194.pdf).
* fastText performs upto 600 times faster at test time.

Moosavi-Dezfooli et al. propose universal adversarial perturbations – perturbations that are image-agnostic. Specifically, they extend the framework for crafting adversarial examples, i.e. by iteratively solving

$\arg\min_r \|r \|_2$ s.t. $f(x + r) \neq f(x)$.

Here, $r$ denotes the adversarial perturbation, $x$ a training sample and $f$ the neural network. Instead of solving this problem for a specific $x$, the authors propose to solve the problem over the full training set, i.e. in each iteration, a different sample $x$ is chosen, one step in the direction of the gradient is taken and the perturbation is updated accordingly. In experiments, they show that these universal perturbations are indeed able to fool networks an several images; in addition, these perturbations are – sometimes – transferable to other networks.

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

In object detection the boost in speed and accuracy is mostly gained through network architecture changes.This paper takes a different route towards achieving that goal,They introduce a new loss function called focal loss.

The authors identify class imbalance as the main obstacle toward one stage detectors achieving results which are as good as two stage detectors.

The loss function they introduce is a dynamically scaled cross entropy loss,Where the scaling factor decays to zero as the confidence in the correct class increases.

They add a modulating factor  as shown in the image below to the cross- entropy loss  https://i.imgur.com/N7R3M9J.png
Which ends up looking like this https://i.imgur.com/kxC8NCB.png
in experiments though they add an additional alpha term to it,because it gives them better results.

**Retina Net**

The network consists of a single unified network which is composed of a backbone network and two task specific subnetworks.The backbone network computes the feature maps for the input images.The first sub-network helps in object classification of the backbone networks output and the second sub-network helps in bounding box regression.
The backbone network they use is Feature Pyramid Network,Which they build on top of ResNet.