TLDR; The authors introduce CopyNet, a variation on the seq2seq that incorporates a "copying mechanism". With this mechanism, the effective vocabulary is the union of the standard vocab and the words in the current source sentence. CopyNet predicts words based on a mixed probability of the standard attention mechanism and a new copy mechanism. The authors show empirically that on toy and summarization tasks CopNet behaves as expected: The decoder is dominated by copy mode when it tries to replicate something from the source.

This paper is based on an intriguing idea of combining state-space models (SSMs) and recurrent neural networks (RNNs). Ideally, it is very much needed: For the sequences which have distinct structure and high variability, probabilistic modelling is a big problem. The handcrafted and parameterised feature representations are widely used to ease the problem and it is customary to develop the probabilistic model on top of these extracted features from the signal (such as using a short-time Fourier transform and _then_ probabilistic modelling over these features).

But machines should be able to handle learning the representation part as well. So the story of the paper. Here, it is termed neural network with stochastic layers but one can safely say that the model is a state-space model with a deterministic neural network layer which is tied to both hidden variables and observations. Still, due to the complicated nonlinearities, it is not easy to understand what's going on.

One thing I found missing from the paper is the exact form of nonlinearity used for the NN layer as I am looking at it from the perspective of probabilistic modelling. But this is probably because of space reasons.

In this article, the authors provide a framework for training two translation models with large accessible monolingual corpus.

In traditional methods, machine translation models always require large parallel corpus to train a good quality model, which is expensive to acquire. However, the massive monolingual data is not fully utilized. The monolingual corpus are typically used in pretraining the NMT decoder rnn and augmenting initial parallel corpus through self-generated translations.

The authors embed machine translation task into a reinforcement learning framework, in which two agents act as two different native speakers respectively and know little about each other and then they learn to translate by trying to communicate with each other. 

**The two speakers**, `A` and `B`, obviously know well about their corresponding language respectively, this situation is easily simulated by two well-trained language models for `A` and `B`. Then, speaker `A` tries to tell a sentence $x$ to `B` by translating it into $y$ in `B`'s language. Since they don't know each other, `B` is uncertain about what `A` truly means by saying $y$. However, `B` is capable of evaluate the degree of sensibility of $y$  from his own understanding. Next, `B` informs `A` his sensibility evaluation score and tries to recover what `A` truly means in `A`'s language, i.e. $x'$. And similarly, `A` can also evaluate the degree of sensibility of $x'$  from his own understanding.

In general, the very original idea that `A` tried to convey, is passed through a noisy channel to `B`, and then back to `A` through another noisy channel. The former noisy channel is a `A-B` translation model and the latter a `B-A` translation model in the framework.

Think about how the first American learnt Chinese in history and I think it is intuitively similar to the principle in this work.

Feinman et al. use dropout to compute an uncertainty measure that helps to identify adversarial examples. Their so-called Bayesian Neural Network Uncertainty is computed as follows:

$\frac{1}{T} \sum_{i=1}^T \hat{y}_i^T \hat{y}_i - \left(\sum_{i=1}^T \hat{y}_i\right)\left(\sum_{i=1}^T \hat{y}_i\right)$

where $\{\hat{y}_1,\ldots,\hat{y}_T\}$ is a set of stochastic predictions (i.e. predictions with different noise patterns in the dropout layers). Here, is can easily be seen that this measure corresponds to a variance computatin where the first term is correlation and the second term is the product of expectations. In Figure 1, the authors illustrate the distributions of this uncertainty measure for regular training samples, adversarial samples and noisy samples for two attacks (BIM and JSMA, see paper for details).

https://i.imgur.com/kTWTHb5.png
Figure 1: Uncertainty distributions for two attacks (BIM and JSMA, see paper for details) and normal samples, adversarial samples and noisy samples.

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

Papernot and McDaniel introduce deep k-nearest neighbors where nearest neighbors are found at each intermediate layer in order to improve interpretbaility and robustness. Personally, I really appreciated reading this paper; thus, I will not only discuss the actually proposed method but also highlight some ideas from their thorough survey and experimental results.

First, Papernot and McDaniel provide a quite thorough survey of relevant work in three disciplines: confidence, interpretability and robustness. To the best of my knowledge, this is one of few papers that explicitly make the connection of these three disciplines. Especially the work on confidence is interesting in the light of robustness as Papernot and McDaniel also frequently distinguish between in-distribution and out-distribution samples. Here, it is commonly known that deep neural networks are over-confidence when moving away from the data distribution.

The deep k-nearest neighbor approach is described in Algorithm 1 and summarized in the following. For a trained model and a training set of labeled samples, they first find k nearest neighbors for each intermediate layer of the network. The layer nonconformity with a specific label $j$, referred to as $\alpha$ in Algorithm 1, is computed as the number of labels that in the set of nearest neighbors that do not share this label. By comparing these nonconformity values to a set of reference values (computing over a set of labeled calibration data), the prediction can be refined. In particular, the probability for label $j$ can be computed as the fraction of reference nonconformity values that are higher than the computed one. See Algorthm 1 or the paper for details.

https://i.imgur.com/RA6q1VI.png
https://i.imgur.com/CkRf8ex.png
Algorithm 1: The deep k-nearest neighbor algorithm and an illustration.

Finally, they provide experimental results – again considering the three disciplines of confidence/credibility, interpretability and robustness. The main take-aways are that the resulting confidences are more reliable on out-of-distribution samples, which also include adversarial examples. Additioanlly, the nearest neighbor allow very basic interpretation of the predictions.

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