Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract ...

Abstract: Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-distribution ...

Before installation, it’s crucial to understand that Microsoft Graph is a RESTful web API that integrates various Microsoft services. You only need to authenticate once to access data across these ...

Point-in-time audits fail in composable, adversarial markets. AI-powered continuous assurance using solvers and simulation replaces episodic security checks. AI for coding has achieved product-market ...

According to @deanmlittle, decentralized exchanges use u128 arithmetic in invariant curve calculations to prevent u64 overflow, a critical implementation detail in DEX pricing logic. Source: ...

The main motivation is that fx passes are really difficult to write when there are dict or dataclass operations in the graph. E.g. the dict lookup below For dicts, this means we would rewrite the ...

module: inductor oncall: pt2 triagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and ...

Fullerenes are hollow carbon molecules where each atom is connected to exactly three other atoms, arranged in pentagonal and hexagonal rings. Mathematically, they can be combinatorially modeled as ...

Abstract: As a promising strategy to achieve generalizable graph learning tasks, graph invariant learning emphasizes identifying invariant subgraphs for stable predictions on biased unknown ...