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:
Most of the existing Graph Neural Networks (GNNs) are the mere extension of the Convolutional Neural Networks (CNNs) to graphs. Generally, they consist of several steps of message passing between the nodes followed by a global indiscriminate feature pooling function. In many data-sets, however, the nodes are unlabeled or their labels provide no information about the similarity between the nodes and the locations of the nodes in the graph. Accordingly, message passing may not propagate helpful information throughout the graph. We show that this conventional approach can fail to learn to perform even simple graph classification tasks. We alleviate this serious shortcoming of the GNNs by making them a two step method. In the first of the proposed approach, a graph embedding algorithm is utilized to obtain a continuous feature vector for each node of the graph. The embedding algorithm represents the graph as a point-cloud in the embedding space. In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method. The GNN learns to perform the given task by inferring the topological structure of the graph encoded in the spatial distribution of the embedded vectors. In addition, we extend the proposed approach to the graph clustering problem and a new architecture for graph clustering is proposed. Moreover, the spatial representation of the graph is utilized to design a graph pooling algorithm. We turn the problem of graph down-sampling into a column sampling problem, i.e., the sampling algorithm selects a subset of the nodes whose feature vectors preserve the spatial distribution of all the feature vectors. We apply the proposed approach to several popular benchmark data-sets and it is shown that the proposed geometrical approach strongly improves the state-of-the-art result for several data-sets. For instance, for the PTC data-set, we improve the state-of-the-art result for more than 22 %.

Ex Output:
The graph analysis problem is transformed into a point cloud analysis problem.

Ex Input:
Analysis methods which enable us to better understand the
representations and functioning of neural models of language are
increasingly needed as deep learning becomes the dominant approach
in NLP. Here we present two methods based on Representational
Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to
directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by
symbolic structures such as syntax trees. We first validate our
methods on the case of a simple synthetic language for arithmetic
expressions with clearly defined syntax and semantics, and show that
they exhibit the expected pattern of results. We then apply our methods to
correlate neural representations of English sentences with their
constituency parse trees.

Ex Output:
Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures.

Ex Input:
In this work, we first conduct mathematical analysis on the memory, which is
defined as a function that maps an element in a sequence to the current output,
of three RNN cells; namely, the simple recurrent neural network (SRN), the long
short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the
analysis, we propose a new design, called the extended-long short-term memory
(ELSTM), to extend the memory length of a cell. Next, we present a multi-task
RNN model that is robust to previous erroneous predictions, called the dependent
bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout
(SISO) problem. Finally, the performance of the DBRNN model with the
ELSTM cell is demonstrated by experimental results.

Ex Output:
A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems