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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Grid-Functioned Neural Networks
Authors: Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth
ICML 2021 | Venue PDF | 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. |