Structure-Aware Convolutional Neural Networks
Authors: Jianlong Chang, Jie Gu, Lingfeng Wang, GAOFENG MENG, SHIMING XIANG, Chunhong Pan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eleven datasets strongly evidence that SACNNs outperform current models on various machine learning tasks, including image classification and clustering, text categorization, skeleton-based action recognition, molecular activity detection, and taxi flow prediction. |
| Researcher Affiliation | Academia | 1NLPR, Institute of Automation, Chinese Academy of Sciences 2School of Artificial Intelligence, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Specifically, Our core code will be released at https://github.com/vector-1127/SACNNs. |
| Open Datasets | Yes | We perform experiments on six Euclidean and five non-Euclidean structured datasets to verify the capability of SACNNs. Six Euclidean structured datasets include the Mnist [26], Cifar-10 [23], Cifar-100 [23], STL-10 [8], Image10 [6], and Image Dog [6] image datasets. Five non-Euclidean structured datasets contain the text categorization datasets 20NEWS and Reuters [25], the action recognition dataset NTU [36], the molecular activity dataset DPP4 [20], and the taxi flow dataset TF-198 [42] that consists of the taxis flow data at 198 traffic intersections in a city. |
| Dataset Splits | Yes | Figure 3 (a) illustrates the validation errors of SACNNs with different t. (b) Influence of channels... validation error (Cifar-10). The paper utilizes standard benchmark datasets (e.g., MNIST, CIFAR-10) that commonly have predefined train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software or libraries used in the experiments. |
| Experiment Setup | Yes | The hyper-parameters in SACNNs are set as follows. In our experiments, the max pooling and the Graclus method [11] are employed as the pooling operations to coarsen the feature maps in SACNNs when managing Euclidean and non-Euclidean structured data respectively, the Re LU function [13] is used as the activation function, batch normalization [17] is employed to normalize the inputs of all layers, parameters are randomly initialized with a uniform distribution U( 0.1, 0.1), the order of polynomials t is set to the maximum number of neighbors among the whole spatial domains (e.g., t = 9 if we attempt to learn 3 3 filters in images). During the training stage, the Adam optimizer [21] with the initial learning rate 0.001 is utilized to train SACNNs, the mini-batch size is set to 32, the categorical cross entropy loss is used in the classification tasks, and the mean squared error loss is used in the regression tasks. |