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].
Porcupine Neural Networks: Approximating Neural Network Landscapes
Authors: Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, our theoretical and empirical results suggest that an unconstrained neural network can be approximated using a polynomially-large PNN. |
| Researcher Affiliation | Academia | Soheil Feizi Department of Computer Science University of Maryland, College Park EMAIL Hamid Javadi Department of Electrical and Computer Engineering Rice University EMAIL Jesse Zhang Department of Electrical Engineering Stanford University EMAIL David Tse Department of Electrical Engineering Stanford University EMAIL |
| Pseudocode | No | The paper does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We provide code for PNN experiments in the following link: https://github.com/jessemzhang/porcupine_neural_networks |
| Open Datasets | Yes | Next, we evaluate PNNs on MNSIT. We ο¬rst trained a dense network on a subset of the MNIST handwritten digits dataset. Of the 10 types of 28x28 MNIST images, we only looked at images of 1 s and 2 s, assigning them the labels of y = 1 and y = 2, respectively. This resulted in n = 11, 649 training samples and 2, 167 test samples. |
| Dataset Splits | No | The paper specifies training and test samples for both synthetic and MNIST datasets but does not explicitly mention or detail a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We train the PNN via stochastic gradient descent using batches of size 100, 100 training epochs, no momentum, and a learning rate of 10 3 which decays every epoch at a rate of 0.95 every 390 epochs. |