Representation Learning via Invariant Causal Mechanisms
Authors: Jovana Mitrovic, Brian McWilliams, Jacob C Walker, Lars Holger Buesing, Charles Blundell
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 {mitrovic, bmcw, jcwalker, lbuesing, cblundell}@google.com |
| 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. |