When you get a demo and something works 90% of the time, that’s just the first nine.” — Andrej Karpathy The “March of Nines” frames a common production reality: You can reach the first 90% reliability ...

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 ...

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 ...

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 ...

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 ...

Graph theory is a mathematical discipline that studies graphs, which are abstract structures used to model and analyze relationships between objects. A Graph ζ = (V (ζ), E (ζ)) is defined to be the ...