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..
Unsupervised Deep Haar Scattering on Graphs
Authors: Xu Chen, Xiuyuan Cheng, Stephane Mallat
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Numerical ExperimentsThe performance of a Haar scattering classification is tested on scrambled images, whose graph geometry is unknown. Results are provided for MNIST and CIFAR-10 image data bases. Classification experiments are also performed on scrambled signals whose samples are on an irregular grid of a sphere. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Princeton University, NJ, USA 2D epartement d Informatique, Ecole Normale Sup erieure, Paris, France |
| Pseudocode | No | The paper describes the computational process through text and equations (e.g., equations 1-5 and Figure 1), but it does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | All computations can be reproduced with a software available at www.di.ens.fr/data/scattering/haar. |
| Open Datasets | Yes | MNIST is a data basis with 6 × 10^4 hand-written digit images of size d = 210, with 5 × 10^4 images for training and 10^4 for testing. CIFAR-10 images are color images of 32 × 32 pixels... with a total of 5 × 10^4 training examples and 10^4 testing examples. |
| Dataset Splits | No | The paper states '5 × 10^4 images for training and 10^4 for testing' for MNIST and CIFAR-10 datasets, but does not explicitly describe a separate validation split or the methodology for such a split. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'All computations can be reproduced with a software available at www.di.ens.fr/data/scattering/haar,' but it does not list specific software dependencies or their version numbers. |
| Experiment Setup | Yes | The scattering scale 2J d is the invariance scale. Scattering coefficients are computed up to the a maximum order m, which is set to 4 in all experiments. Indeed, higher order scattering coefficient have a negligible relative energy, which is below 1%. The unsupervised learning algorithm computes N multiresolution approximations, corresponding to N different scattering transforms... The supervised dimension reduction selects a final set of M orthogonalized scattering coefficients. We set M = 1000 in all numerical experiments. |