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 [1].
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. |