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.
Learning to Combine Per-Example Solutions for Neural Program Synthesis
Authors: Disha Shrivastava, Hugo Larochelle, Daniel Tarlow
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,386 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,855 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 | Evaluation across programs of different lengths and under two different experimental settings reveal that when given the same time budget, our technique significantly improves the success rate over PCCoder [30] and other ablation baselines. The code, data and trained models for our work can be found at: https://github.com/shrivastavadisha/N-PEPS. |
| Researcher Affiliation | Collaboration | Disha Shrivastava Mila, Université de Montréal Google Research Hugo Larochelle Mila, Université de Montréal Google Research CIFAR Fellow Daniel Tarlow Mila, Mc Gill University Google Research |
| Pseudocode | No | The paper describes algorithms and processes (e.g., PCCoder, CAB), but does not contain a formal 'Pseudocode' or 'Algorithm' block/figure. |
| Open Source Code | Yes | The code, data and trained models for our work can be found at: https://github.com/shrivastavadisha/N-PEPS. |
| Open Datasets | Yes | The code, data and trained models for our work can be found at: https://github.com/shrivastavadisha/N-PEPS. |
| Dataset Splits | Yes | 10% of the training data was used for validation. |
| Hardware Specification | Yes | To account for variability across machines, we chose to run a test split on a machine chosen randomly from a collection of 7 machines of similar configuration (Google Cloud instances with 120GB RAM each)4. |
| Software Dependencies | No | The paper mentions using the PCCoder implementation but does not list specific software dependencies (e.g., Python, PyTorch, CUDA) with version numbers in the main text. |
| Experiment Setup | No | The paper states that 'Complete details of hyperparameters for all methods can be found in Appendix D.' but does not list specific hyperparameters or system-level training settings in the main text. |