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..
Deep Semi-Random Features for Nonlinear Function Approximation
Authors: Kenji Kawaguchi, Bo Xie, Le Song
AAAI 2018 | Venue PDF | 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. |