Deep Learning with Topological Signatures
Authors: Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Classification experiments on 2D object shapes and social network graphs demonstrate the versatility of the approach and, in case of the latter, we even outperform the state-of-the-art by a large margin. 5 Experiments To demonstrate the versatility of the proposed approach, we present experiments with two totally different types of data: (1) 2D shapes of objects, represented as binary images and (2) social network graphs, given by their adjacency matrix. In both cases, the learning task is classification. |
| Researcher Affiliation | Academia | Christoph Hofer Department of Computer Science University of Salzburg, Austria chofer@cosy.sbg.ac.at Roland Kwitt Department of Computer Science University of Salzburg, Austria Roland.Kwitt@sbg.ac.at Marc Niethammer UNC Chapel Hill, NC, USA mn@cs.unc.edu Andreas Uhl Department of Computer Science University of Salzburg, Austria uhl@cosy.sbg.ac.at |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly labeled or presented in the paper. |
| Open Source Code | Yes | Source code is publicly-available at https://github.com/c-hofer/nips2017. |
| Open Datasets | Yes | We apply persistent homology combined with our proposed input layer to two different datasets of binary 2D object shapes: (1) the Animal dataset, introduced in [3] which consists of 20 different animal classes, 100 samples each; (2) the MPEG-7 dataset which consists of 70 classes of different object/animal contours, 20 samples each (see [21] for more details). ... We evaluate our approach on the challenging problem of social network classification, using the two largest benchmark datasets from [31], i.e., reddit-5k (5 classes, 5k graphs) and reddit-12k (11 classes, 12k graphs). |
| Dataset Splits | No | In each experiment we ensured a balanced group size (per label) and used a 90/10 random training/test split; all reported results are averaged over five runs with fixed ν = 0.1. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | All experiments were implemented in Py Torch3, using DIPHA4 and Perseus [23]. |
| Experiment Setup | Yes | We use cross-entropy loss to train the network for 400 epochs, using stochastic gradient descent (SGD) with mini-batches of size 128 and an initial learning rate of 0.1 (halved every 25-th epoch). ... We train the network for 500 epochs using SGD and cross-entropy loss with an initial learning rate of 0.1 (reddit_5k), or 0.4 (reddit_12k). |