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.
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Question: A number of recent methods to understand neural networks have focused on quantifying the role of individual features.   One such method, NetDissect identifies interpretable features of a model using the Broden dataset of visual semantic labels (colors, materials, textures, objects and scenes).   Given the recent rise of a number of action recognition datasets, we propose extending the Broden dataset to include actions to better analyze learned action models.   We describe the annotation process, results from interpreting action recognition models on the extended Broden dataset and examine interpretable feature paths to help us understand the conceptual hierarchy used to classify an action.

Answer: We expand Network Dissection to include action interpretation and examine interpretable feature paths to understand the conceptual hierarchy used to classify an action.


Question: 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 %.

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


Question: Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating the BN’s cost by using only a small fraction of data for mean & variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation while the efficiency expects regular execution pattern. To this end, we propose two categories of approach: sampling or creating few uncorrelated data for statistics’ estimation with certain strategy constraints. The former includes “Batch Sampling (BS)” that randomly selects few samples from each batch and “Feature Sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “Virtual Dataset Normalization (VDN)” that generates few synthetic random samples. Accordingly, multi-way strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. All the proposed methods are comprehensively evaluated on various DNN models, where an overall training speedup by up to 21.7% on modern GPUs can be practically achieved without the support of any specialized libraries, and the loss of model accuracy and convergence rate are negligible. Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-batch normalization” problem in the case of tiny batch size.

Answer:
We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations  with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively.