Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the gr...
Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between the graph structure of the neural network and its predictive performance. Here we systematically investigate how does the gr...
interconnected by the spatial anatomical layout, can be interpreted as time-varying graph structured signals. For such applications, graph neural networks (GNN) have shown to be useful, providing a
meaning that they have dissimilar features and different class labels [29]. Many real-world graphs, such as transaction networks [45], exhibit heterophily. Recent studies have shown that GNNs do not perform well on heterophilic graphs [46,47,48,49]. This is because GNNs are typically design...
networks FDL gets trapped into a local sub-optimal configuration, successfully avoided by NeuLay-2. This ratio is especially large and increasing withNfor BA and ER graphs, indicating that while NeuLay-2 may not show as high speedup over FDL for these networks as it does for more structured...
networks and graph attention networks in Section 2.2.2 as they contribute to the propagation step. We present the graph spatial-temporal networks in Section 2.2.1 as the models are usually used on dynamic graphs. We introduce graph auto-encoders in Sec 2.2.3 as they are trained in an ...
is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing 9 between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can repres...
API for building custom pipelines, providing multiple data conversion interfaces: map, filter, flat_map, concat, window, time_window, and window_all. Through these interfaces, complex data processing pipelines can be built quickly to process unstructured data such as video, audio, text, images, ...
spatial-temporal networks in Section 2.2.1 as the models are usually used on dynamic graphs. We introduce graph auto-encoders in Sec 2.2.3 as they are trained in an unsupervised fashion. And finally, we introduce graph generative networks in applications of graph generation (see Section 3.3.1...
Although there are some important differences between saccades and smooth pursuit eye movements (e.g., latencies of pursuit and saccades tend to be different), both types of eye movements are controlled by largely overlapping neural networks at the neurophysiological level18,19, and the two types...