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].
On Contrastive Representations of Stochastic Processes
Authors: Emile Mathieu, Adam Foster, Yee Teh
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our methods are effective for learning representations of periodic functions, 3D objects and dynamical processes. |
| Researcher Affiliation | Collaboration | Emile Mathieu , Adam Foster , Yee Whye Teh , EMAIL, Department of Statistics, University of Oxford, United Kingdom Deep Mind, United Kingdom |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at github.com/ae-foster/cresp. |
| Open Datasets | Yes | We apply CRESP to Shape Net (Chang et al., 2015), a standard dataset in the ๏ฌeld of 3D object representations. |
| Dataset Splits | No | The paper mentions 'training views' and 'test views' but does not specify clear train/validation/test dataset splits with percentages, absolute counts, or references to predefined validation sets. |
| Hardware Specification | No | The paper does not specify the exact hardware used for experiments (e.g., specific GPU or CPU models, memory, or cluster specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Please refer to Appendix D for full experimental details. [...] We train all models for 200 epochs, varying the distance between modes and the number of training context points. [...] They are trained for 200 epochs, with contexts of 5 randomly sampled pairs {yi = F(xi), xi U([0, 1])}. |