Grid-Functioned Neural Networks
Authors: Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a full characterisation of its computational and spatial complexity, and demonstrate its potential, compared to a traditional architecture, over a set of synthetic regression problems. We further illustrate the benefits through a real-world 3D skeletal animation case study |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, University of Bath, Bath, UK 2 Ninja Theory, Cambridge, UK. |
| Pseudocode | No | The paper provides mathematical definitions and equations for the model but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We use the same dataset and input and output encoding presented by Zhang et al. (2018), changing only the model doing the prediction. |
| Dataset Splits | Yes | For each problem, 200 points are randomly sampled, 80% of which are used for training and 20% for evaluation. |
| Hardware Specification | Yes | Evaluation time measured on an Intel Core i7-7700K CPU running at 4.20 GHz. |
| Software Dependencies | No | The paper mentions using “Adam optimisation” and “Re LU activation” but does not provide specific version numbers for any software dependencies, libraries, or frameworks. |
| Experiment Setup | Yes | Every model was trained on each problem to minimise the mean squared error at the output using Adam optimisation (Kingma & Ba, 2015). The training ran for 100 000 steps on batches of 32 examples per step with a fixed learning rate of 0.001. |