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..
Functional Regularisation for Continual Learning with Gaussian Processes
Authors: Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now test the scalability and competitiveness of our method on various continual learning problems, referring to the proposed approach as Functional Regularised Continual Learning (FRCL). |
| Researcher Affiliation | Collaboration | Michalis K. Titsias Deep Mind EMAIL Jonathan Schwarz Deep Mind & University College London EMAIL Alexander G. de G. Matthews Deep Mind EMAIL Razvan Pascanu Deep Mind EMAIL Yee Whye Teh Deep Mind EMAIL |
| Pseudocode | Yes | Algorithm 1 Functional Regularised Continual Learning (FRCL) with task boundary detection |
| Open Source Code | No | The paper mentions that FRCL methods were implemented using GPflow and that VCL results were obtained using code provided by other authors, but does not provide a link or explicit statement that their own implementation code for FRCL is open-source. |
| Open Datasets | Yes | We consider experiments on three established Continual Learning classification problems: Split-MNIST, Permuted-MNIST and sequential Omniglot (Goodfellow et al., 2013; Zenke et al., 2017; Schwarz et al., 2018), described in the Appendix. |
| Dataset Splits | Yes | Note that for the MNIST results, we obtain final results after optimising hyperparameters on the validation set and using those values to train on the union of training & validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU models, CPU specifications, or memory, for running the experiments. |
| Software Dependencies | No | FRCL methods have been implemented using GPflow (Matthews et al., 2017). The paper mentions GPflow as a library but does not provide its version number or version numbers for any other software dependencies. |
| Experiment Setup | Yes | Experimental details for all experiments are shown in Tables 4, 5 and 6. |