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 [1].
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]. |