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Paszke and colleagues: PyTorch imperative deep-learning runtime

Contribution grade

Machine-learning systems and compilers · Human-designed research method or resource

Reference score225,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined an imperative Python execution model with automatic differentiation and efficient accelerator-aware runtime components.

Scientific scope

Combined an imperative Python execution model with automatic differentiation and efficient accelerator-aware runtime components. Year denotes this system paper, not the first software release. The paper does not invent automatic differentiation or guarantee every eager program matches compiled performance.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Year denotes this system paper, not the first software release. The paper does not invent automatic differentiation or guarantee every eager program matches compiled performance.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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