Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On Contrastive Representations of Stochastic Processes
Authors: Emile Mathieu, Adam Foster, Yee Teh
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,974 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,863 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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])}. |