Porcupine Neural Networks: Approximating Neural Network Landscapes
Authors: Soheil Feizi, Hamid Javadi, Jesse Zhang, David Tse
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 sfeizi@cs.umd.edu Hamid Javadi Department of Electrical and Computer Engineering Rice University hrhakim@rice.edu Jesse Zhang Department of Electrical Engineering Stanford University jessez@stanford.edu David Tse Department of Electrical Engineering Stanford University dntse@stanford.edu |
| 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 first 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. |