Efficient Target Propagation by Deriving Analytical Solution
Authors: Yanhao Bao, Tatsukichi Shibuya, Ikuro Sato, Rei Kawakami, Nakamasa Inoue
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |