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: Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as ''neural speed reading'', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word.
 A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that 
 Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.
Example Output: We propose a new model for neural speed reading that utilizes the inherent punctuation structure of a text to define effective jumping and skipping behavior.

Example Input: Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a novel graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while also often generating embeddings of better quality for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation.
Example Output: A generic framework to scale existing graph embedding techniques to large graphs.

Example Input: This paper explores the scenarios under which
 an attacker can claim that ‘Noise and access to
 the softmax layer of the model is all you need’
 to steal the weights of a convolutional neural network
 whose architecture is already known. We
 were able to achieve 96% test accuracy using
 the stolen MNIST model and 82% accuracy using
 stolen KMNIST model learned using only
 i.i.d. Bernoulli noise inputs. We posit that this
 theft-susceptibility of the weights is indicative
 of the complexity of the dataset and propose a
 new metric that captures the same. The goal of
 this dissemination is to not just showcase how far
 knowing the architecture can take you in terms of
 model stealing, but to also draw attention to this
 rather idiosyncratic weight learnability aspects of
 CNNs spurred by i.i.d. noise input. We also disseminate
 some initial results obtained with using
 the Ising probability distribution in lieu of the i.i.d.
 Bernoulli distribution
Example Output:
Input only noise , glean the softmax outputs, steal the weights