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..
Representation Learning via Invariant Causal Mechanisms
Authors: Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, Charles Blundell
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, RELIC significantly outperforms competing methods in terms of robustness and out-of-distribution generalization on Image Net, while also significantly outperforming these methods on Atari achieving above human-level performance on 51 out of 57 games. |
| Researcher Affiliation | Industry | Jovana Mitrovic Brian Mc Williams Jacob Walker Lars Buesing Charles Blundell Deep Mind London, UK EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | For the prediction tasks, we test RELIC after pretraining the representation in a self-supervised way on the training set of the Image Net ILSVRC-2012 dataset (Russakovsky et al., 2015). ... Specifically, we test RELIC on the suite of Atari games (Bellemare et al., 2013) which consists of 57 diverse games of varying difficulty. |
| Dataset Splits | No | The paper mentions using the 'Image Net validation set' but does not provide specific details on its split (e.g., percentages, sample counts, or explicit splitting methodology) within the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper refers to external procedures and appendices (e.g., 'following the procedure in (Kolesnikov et al., 2019; Chen et al., 2020a) and Appendix E.4.') but does not explicitly state concrete hyperparameter values or detailed training configurations within the main text. |