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.

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

Example Input: The sign stochastic gradient descent method (signSGD) utilizes only the sign of the stochastic gradient in its updates. Since signSGD carries out one-bit quantization of the gradients, it is extremely practical for distributed optimization where gradients need to be aggregated from different processors. For the first time, we establish convergence rates for signSGD on general non-convex functions under transparent conditions. We show that the rate of signSGD to reach first-order critical points matches that of SGD in terms of number of stochastic gradient calls, up to roughly a linear factor in the dimension. We carry out simple experiments to explore the behaviour of sign gradient descent (without the stochasticity) close to saddle points and show that it often helps completely avoid them without using either stochasticity or curvature information.
Example Output: We prove a non-convex convergence rate for the sign stochastic gradient method. The algorithm has links to algorithms like Adam and Rprop, as well as gradient quantisation schemes used in distributed machine learning.

Example Input: Distributional Semantics Models(DSM) derive word space from linguistic items
in context. Meaning is obtained by defining a distance measure between vectors
corresponding to lexical entities. Such vectors present several problems. This
work concentrates on quality of word embeddings, improvement of word embedding
vectors, applicability of a novel similarity metric used 'on top' of the
word embeddings. In this paper we provide comparison between two methods
for post process improvements to the baseline DSM vectors. The counter-fitting
method which enforces antonymy and synonymy constraints into the Paragram
vector space representations recently showed improvement in the vectors' capability
for judging semantic similarity. The second method is our novel RESM
method applied to GloVe baseline vectors. By applying the hubness reduction
method, implementing relational knowledge into the model by retrofitting synonyms
and providing a new ranking similarity definition RESM that gives maximum
weight to the top vector component values we equal the results for the ESL
and TOEFL sets in comparison with our calculations using the Paragram and Paragram
+ Counter-fitting methods. For SIMLEX-999 gold standard since we cannot
use the RESM the results using GloVe and PPDB are significantly worse compared
to Paragram. Apparently, counter-fitting corrects hubness. The Paragram
or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999
gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense
de-conflation (that was announced to be state-of the-art method for less reliable
gold standards). Apparently relational knowledge and counter-fitting is more important
for judging semantic similarity than sense determination for words. It is to
be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999
results. The lesson is that many corrections to word embeddings are necessary
and methods with more parameters and hyperparameters perform better.

Example Output:
Paper provides a description of a procedure to enhance word vector space model with an evaluation of Paragram and GloVe models for Similarity Benchmarks.