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..
Batch normalization is sufficient for universal function approximation in CNNs
Authors: Rebekka Burkholz
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our theory, we explicitly match target networks that outperform experimentally obtained networks with trained BN parameters by utilizing a sufficient number of random features. ... 3 EXPERIMENTS |
| Researcher Affiliation | Academia | Rebekka Burkholz CISPA Helmholtz Center for Information Security 66123 Saarbrücken, Germany EMAIL |
| Pseudocode | No | The paper discusses reconstruction algorithms but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the described methodology. |
| Open Datasets | Yes | We consider two standard image classifcation benchmark datasets, CIFAR10 and CIFAR100 (Krizhevsky, 2009) |
| Dataset Splits | No | The paper mentions using CIFAR10 and CIFAR100, which have standard splits, but does not explicitly state the training/validation/test dataset splits (e.g., percentages or sample counts) within the text. |
| Hardware Specification | Yes | All experiments were conducted on a machine with Intel(R) Core(TM) i9-10850K CPU @ 3.60GHz processor and GPU NVIDIA Ge Force RTX 3080 Ti |
| Software Dependencies | No | The paper mentions 'pytorchs torch.linalg.solve function' and 'Pytorchs torch.optim.LBFGS', indicating the use of PyTorch, but it does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We use a standard training procedure: SGD with 5 warmup epochs and linear learning rate increase, followed by 200 epochs of cosine annealing with initial learning rate 0.1. |