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..
Efficient Target Propagation by Deriving Analytical Solution
Authors: Yanhao Bao, Tatsukichi Shibuya, Ikuro Sato, Rei Kawakami, Nakamasa Inoue
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments, we have validated the effectiveness of this approach. Using the CIFAR-10 dataset, our method showcases accuracy levels comparable to state-of-the-art TP methods. |
| Researcher Affiliation | Collaboration | 1 Tokyo Institute of Technology 2 Denso IT Laboratory |
| Pseudocode | Yes | Algorithm 1: Local Difference Reconstruction Loss (LDRL) ... Algorithm 2: Analytical Feedback Function |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Using the CIFAR-10 dataset, our method showcases accuracy levels comparable to state-of-the-art TP methods. ... We conduct experiments with Le Net (Le Cun et al. 1989), Simplified-VGG (a simplified version of VGGNet (Simonyan and Zisserman 2015)), MLPMixer (Tolstikhin et al. 2021) on MNIST, Fashion-MNIST and CIFAR-10 datasets. |
| Dataset Splits | No | The paper mentions using MNIST, Fashion-MNIST, and CIFAR-10 datasets, and presents 'Train' and 'Test' accuracies in tables, but does not provide specific details on the training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' in the context of matrix inversion, but does not specify its version number or any other software dependencies with their respective versions. |
| Experiment Setup | No | The paper discusses the network architectures (Le Net, Simplified-VGG, MLPMixer) and evaluation datasets, but does not provide specific details on experimental setup parameters such as learning rates, batch sizes, number of epochs, or optimizer configurations. |