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..
White Noise Analysis of Neural Networks
Authors: Ali Borji, Sikun Lin
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments over four classic datasets, MNIST, Fashion-MNIST, CIFAR-10, and Image Net, show that the computed bias maps resemble the target classes and when used for classification lead to an over two-fold performance than the chance level. |
| Researcher Affiliation | Collaboration | Ali Borji & Sikun Lin University of California, Santa Barbara, CA EMAIL, EMAIL Work done during internship at Markable AI. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code is available at: https://github.com/aliborji/White Noise Analysis.git. |
| Open Datasets | Yes | Over four datasets, MNIST (Le Cun et al., 1998), Fashion-MNIST (Xiao et al., 2017), CIFAR-10 (Krizhevsky et al., 2009), and Image Net Deng et al. (2009), we employ classification images to discover implicit biases of a network... |
| Dataset Splits | Yes | We conducted an experiment on Image Net validation set including 50K images covering 1000 categories and 1 million samples using Gabor PCA sampling... |
| Hardware Specification | No | The paper mentions running experiments on 'a single GPU' but does not specify the model or manufacturer (e.g., NVIDIA A100, RTX 2080 Ti), making the hardware specification too vague. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We trained a CNN with 2 conv layers, 2 pooling layers, and one fully connected layer (see supplement Fig. 10) on the MNIST dataset. ... We changed the activation functions in the convolution layers of the CIFAR-10 CNN model to tanh, as using Re LU activation resulted in some dead filters. ... here we use λl = 0.01, 0.1, 1 for fc, conv1, and conv2, respectively. |