Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Credit Assignment Through Broadcasting a Global Error Vector
Authors: David Clark, L F Abbott, Sueyeon Chung
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that this form of global-error learning is surprisingly powerful, performing on par with BP in VNNs and overcoming DFA s inability to train convolutional layers. ... Here, we show that GEVB performs well in practice. |
| Researcher Affiliation | Academia | David G. Clark, L.F. Abbott, Sue Yeon Chung Center for Theoretical Neuroscience Columbia University New York, NY EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code accompanying our paper is available at https://github.com/davidclark1/Vectorized Nets. |
| Open Datasets | Yes | We trained models on MNIST [37] and CIFAR-10 [38] |
| Dataset Splits | No | The paper does not explicitly provide details about training/test/validation dataset splits, such as percentages or sample counts for a validation set. |
| Hardware Specification | Yes | Training lasted 10 days using five NVIDIA GTX 1080 Ti GPUs. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as Python or PyTorch versions, only mentions the use of 'Adam' as an optimizer without a version. |
| Experiment Setup | Yes | We used Adam for a fixed number of epochs (namely, 190), stopping early at zero training error. For each experiment, we performed five random initializations. Mixed-sign networks were initialized using He initialization, and nonnegative networks were initialized using ON/OFF initialization with an underlying He initialization [36]. |