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Found 4 papers, 1 papers with code
On the expressivity of deep Heaviside networks
We show that deep Heaviside networks (DHNs) have limited expressiveness but that this can be overcome by including either skip connections or neurons with linear activation.
InterChat: Enhancing Generative Visual Analytics using Multimodal Interactions
To address these limitations, we investigate the design space of multimodal interactions for generative visual analytics through a literature review and pilot brainstorming sessions.
Understanding the Effect of GCN Convolutions in Regression Tasks
Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs.
SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization
The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals.
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