Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
Authors: Georgios Smyrnis, Petros Maragos
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we make experimental evaluations on the MNIST and Fashion-MNIST datasets, with our results demonstrating a significant reduction in network size, while retaining adequate performance. |
| Researcher Affiliation | Academia | School of ECE, National Technical University of Athens, Athens, Attiki, Greece. Also: Robot Perception and Interaction Unit, Athena Research Center, Maroussi, Greece. |
| Pseudocode | Yes | Algorithm 1 Heuristic Minimization Algorithm (Smyrnis et al., 2020), Algorithm 2 One-Versus-All Multiclass Minimization, Algorithm 3 Stable Divisor Picking Algorithm |
| Open Source Code | Yes | The code used is provided as supplementary material, and can also be found here: https://github.com/Georgios Smyrnis/ multiclass_minimization_icml2020. |
| Open Datasets | Yes | We made use of the MNIST dataset of handwritten digits (Lecun et al., 1998), as well as Fashion-MNIST (Xiao et al., 2017), to evaluate our methods (available at http://yann.lecun.com/exdb/mnist/ and https://github.com/zalandoresearch/ fashion-mnist, respectively). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about a validation dataset split. |
| Hardware Specification | Yes | The experiments were performed on a Core i5-7200U CPU clocked at 2.5GHz, with 8GB of RAM and 8GB of swap space. |
| Software Dependencies | No | For our experiments we made use of Keras for writing our code (Chollet et al., 2015). No specific version number for Keras or other software dependencies is provided. |
| Experiment Setup | Yes | For the MNIST dataset, the network consisted of two convolutional layers, each with 16 units, 5 5 kernels, Re LU activations and maxpooling with a factor of 3. The output of these layers was then fed to a two-layer feedforward network, with Re LU activations and 500 neurons in the hidden layer. For the Fashion-MNIST dataset, the structure was similar, except the convolutional layers contained 32 units and the hidden layer in the end 1000 neurons. |