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..
Learning Equations for Extrapolation and Control
Authors: Subham Sahoo, Christoph Lampert, Georg Martius
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental evaluation, In Fig. 5 the numerical results and also an illustrative output of EQL and the baselines are presented. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology, Kharagpur, India 2IST Austria, Klosterneuburg, Austria 3Max Planck Institute for Intelligent Systems, Tübingen, Germany. |
| Pseudocode | No | The paper describes the method using diagrams and text, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and some data is available at https://github.com/martius-lab/EQL. |
| Open Datasets | No | The paper describes generating its own training data by sampling points and adding noise, but does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | Yes | For all experiments, we have training data in a restricted domain, usually [ 1, 1]d corrupted with noise which is split into training and validation with 90% 10% split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states it was implemented in 'python based on the theano framework', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The following hyper-parameters were fixed: learningrate (Adam) α = 0.001, regularization (Adam) of ϵ = 0.0001, minibatch size of 20, number of units u = v = 10, i. e. 10 units per type in each layer. We use t1 = 1 4T and t2 = 19 20T, where T is the total number of epochs, large enough to ensure convergence, i. e. T = (L 1) 10000. Note, that early stopping will be disadvantageous. |