AWhat changed
Used binary weights during propagation while accumulating real-valued updates, then extended to binary activations with straight-through gradient approximations and bit-operation inference kernels.
Scientific scope
Used binary weights during propagation while accumulating real-valued updates, then extended to binary activations with straight-through gradient approximations and bit-operation inference kernels. Original BinaryConnect three-author and BinaryNet v1 two-author source sets retained rather than later expanded author lists. Gradients/learning state are not all binary; straight-through estimation is inherited. Kernel-level speedup versus an unoptimized comparator does not establish arbitrary application speed/accuracy or universal exact gradients.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Original BinaryConnect three-author and BinaryNet v1 two-author source sets retained rather than later expanded author lists. Gradients/learning state are not all binary; straight-through estimation is inherited. Kernel-level speedup versus an unoptimized comparator does not establish arbitrary application speed/accuracy or universal exact gradients.
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.
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations
- BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
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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