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 [1].
On the Number of Linear Regions of Deep Neural Networks
Authors: Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically examined the behavior of a trained MLP to see if it folds the input-space in the way described above. |
| Researcher Affiliation | Academia | Guido Mont ufar Max Planck Institute for Mathematics in the Sciences EMAIL Razvan Pascanu Universit e de Montr eal EMAIL Kyunghyun Cho Universit e de Montr eal EMAIL Yoshua Bengio Universit e de Montr eal, CIFAR Fellow EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is openly available. |
| Open Datasets | No | The paper mentions an 'Empirical Evaluation of Folding in Rectifier MLPs' and refers to 'training example' and 'inputs identified by a deep MLP', but it does not specify any named public dataset or provide access information for the data used in this empirical examination. |
| Dataset Splits | No | The paper describes an 'Empirical Evaluation' involving tracing activations and inspecting examples, but it does not provide specific details on training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper describes an 'Empirical Evaluation' but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or training configurations. |