Batch normalization is sufficient for universal function approximation in CNNs
Authors: Rebekka Burkholz
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 burkholz@cispa.de |
| 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. |