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 Q]: 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.
[EX A]: We expand Network Dissection to include action interpretation and examine interpretable feature paths to understand the conceptual hierarchy used to classify an action.

[EX Q]: Designing of search space is a critical problem for neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms. This search space facilitates direct selection of channel numbers and kernel sizes in convolutions. In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training. The algorithm is further accelerated by a dynamic network shrinkage technique.
 Instead of a  search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. 
 Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost.
 We open our entire codebase at: https://github.com/meijieru/AtomNAS.
[EX A]: A new state-of-the-art on Imagenet for mobile setting

[EX Q]: Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Current GCNs also restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We explicitly model different types of edges: annotated edges, learned edges with abstract meaning, and hierarchical edges. We also experiment with different ways to fuse the representations extracted from different edge types. This restriction is sometimes implied from a dataset, however, we relax this restriction for all kinds of datasets. We achieve state-of-the-art results on a variety of chemical, social, and vision graph classification benchmarks.
[EX A]:
A novel approach to graph classification based on spectral graph convolutional networks and its extension to multigraphs with learnable relations and hierarchical structure. We show state-of-the art results on chemical, social and image datasets.