Deep Semi-Random Features for Nonlinear Function Approximation
Authors: Kenji Kawaguchi, Bo Xie, Le Song
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare semi-random features with random features (RF) and neural networks with Re LU on both UCI datasets and image classification benchmarks. |
| Researcher Affiliation | Academia | Kenji Kawaguchi Massachusetts Institute of Technology Bo Xie, Le Song Georgia Institute of Technology |
| Pseudocode | No | The paper describes the mathematical formulations of the models but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of the proposed method is publicly available at: http://github.com/zixu1986/semi-random. |
| Open Datasets | Yes | We compare semi-random features with random features (RF) and neural networks with Re LU on both UCI datasets and image classification benchmarks. [...] MNIST is a popular dataset for recognizing handwritten digits. [...] CIFAR 10 contains internet images [...] The Street View House Numbers (SVHN) dataset contains house digits collected by Google Street View. |
| Dataset Splits | No | The paper provides training and test set sizes (e.g., "60,000 for training and 10,000 for test" for MNIST), but does not explicitly mention a separate validation split or cross-validation strategy in the main text for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper mentions using "tensorflow" for experiments but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The network architecture used on this dataset is multi-layer networks with l = [1, 2, 4] hidden layers and k = [1, 2, 4, 8, 16] d hidden units per layer where d is the input data dimension. [...] We use a convolution neural network consisting of two convolution layers, with 5 5 filters and the number of channels is 32 and 64, respectively. Each convolution is followed by a maxpooling layer, then finally a fully-connected layer of 512 units with 0.5 dropout. |