Chainer dynamic graphs, memory-aware autodiff and CuPy integration

What changed

Documents define-by-run model construction, memory-aware reverse/double backpropagation and NumPy-like CuPy GPU integration in the Chainer framework.

Scientific scope

Documents define-by-run model construction, memory-aware reverse/double backpropagation and NumPy-like CuPy GPU integration in the Chainer framework. The 2019 account retrospectively describes the 2015 Chainer launch and the 2017 separation of CuPy. Later capabilities are not backdated. Dynamic computation and automatic differentiation have predecessors; the contribution is their define-by-run framework integration.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The 2019 account retrospectively describes the 2015 Chainer launch and the 2017 separation of CuPy. Later capabilities are not backdated. Dynamic computation and automatic differentiation have predecessors; the contribution is their define-by-run framework integration.

Contributors

• Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

• Chainer: A Deep Learning Framework for Accelerating the Research Cycle

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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