Reinforcing Neural Network Stability with Attractor Dynamics
Authors: Hanming Deng, Yang Hua, Tao Song, Zhengui Xue, Ruhui Ma, Neil Robertson, Haibing Guan3765-3772
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through intensive experiments, we show that RMAN-modified attractor dynamics bring a more structured representation space to Res Net and its variants, and more importantly improve the generalization ability of Res Net-like networks in supervised tasks due to reinforced stability. |
| Researcher Affiliation | Academia | 1Shanghai Jiao Tong University, {denghanmig, songt333, zhenguixue, ruhuima, hbguan}@sjtu.edu.cn 2Queen s University Belfast, {Y.Hua, N.Robertson}@qub.ac.uk |
| Pseudocode | No | The paper includes mathematical equations (e.g., Eq. 1-6) but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We conduct various experiments on the datasets for various tasks, including CIFAR10, CIFAR100 (Krizhevsky and Hinton 2009) for image classification and Google Commands (Warden 2017) for audio classification. |
| Dataset Splits | No | The paper describes data augmentation and training parameters, but does not provide explicit numerical details on dataset splits (e.g., percentages or sample counts) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using SGD for training but does not provide specific software dependencies or version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | We train all the networks using SGD with a batch size of 128 and momentum of 0.9. The learning rate starts with 0.1 and is devided by 10 at epoch 80/150, 120/225 and training terminates at epoch 160/300 on CIFAR10/CIFAR100, respectively. We apply a weight decay of 0.0005 for all networks. |