Mistakes happen, he theorized, because multiple vulnerabilities in a system align — like the holes in cheese — to create a recipe for disaster. By Michael S. Rosenwald The story of how James ...

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Abstract: In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled ...

Abstract: In this paper, we study the zero-error capacity of channels with memory, which are represented by graphs. We provide a method to construct code for any ...

We formalize the generalization error bound using Rademacher complexity in the Lean 4 theorem prover. Generalization error quantifies the gap between a learning ...

The Graph offers access to competitive and cost-efficient decentralized data sets. The network boasts a 99.99% uptime and 24/7 availability. Central to The Graph’s operations are subgraphs, APIs that ...