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.

Ex Input:
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.

Ex Output:
We introduce the Recurrent Discounted Unit which applies attention to any length sequence in linear time


Ex Input:
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

Ex Output:
We propose a framework to generate “natural” adversaries against black-box classifiers for both visual and textual domains, by doing the search for adversaries in the latent semantic space.


Ex Input:
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

Ex Output:

We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription.