In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Q: Recurrent Neural Networks architectures excel at processing sequences by
modelling dependencies over different timescales. The recently introduced
Recurrent Weighted Average (RWA) unit captures long term dependencies
far better than an LSTM on several challenging tasks. The RWA achieves
this by applying attention to each input and computing a weighted average
over the full history of its computations. Unfortunately, the RWA cannot
change the attention it has assigned to previous timesteps, and so struggles
with carrying out consecutive tasks or tasks with changing requirements.
We present the Recurrent Discounted Attention (RDA) unit that builds on
the RWA by additionally allowing the discounting of the past.
We empirically compare our model to RWA, LSTM and GRU units on
several challenging tasks. On tasks with a single output the RWA, RDA and
GRU units learn much quicker than the LSTM and with better performance.
On the multiple sequence copy task our RDA unit learns the task three
times as quickly as the LSTM or GRU units while the RWA fails to learn at
all. On the Wikipedia character prediction task the LSTM performs best
but it followed closely by our RDA unit. Overall our RDA unit performs
well and is sample efficient on a large variety of sequence tasks.

A: We introduce the Recurrent Discounted Unit which applies attention to any length sequence in linear time
****
Q: Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model.
On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.

A: A simple architecture consisting of convolutions and attention achieves results on par with the best documented recurrent models.
****
Q: In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions. We generate these examples through interpolations in latent space. We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature space via latent code interpolations. We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision.
Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.

A:
We generate examples to explain a classifier desicion via interpolations in latent space. The variational auto encoder cost is extended with a functional of the classifier over the generated example path in data space.
****