Adaptive Neural Compilation
Authors: Rudy R. Bunel, Alban Desmaison, Pawan K. Mudigonda, Pushmeet Kohli, Philip Torr
NeurIPS 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate. |
| Researcher Affiliation | Collaboration | Rudy Bunel Alban Desmaison University of Oxford University of Oxford EMAIL EMAIL Pushmeet Kohli Philip H.S. Torr M. Pawan Kumar Microsoft Research University of Oxford University of Oxford EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Figure 2b presents an 'Intermediary representation' which is a structured, step-by-step description of an algorithm in a code-like format. |
| Open Source Code | Yes | All the code required to reproduce these experiments is available online 1. 1https://github.com/alban D/adaptive-neural-compilation |
| Open Datasets | No | The paper describes tasks (e.g., Access, Swap) and refers to prior work for tasks (Kurach et al. [8]), but does not provide concrete access information (link, DOI, formal citation for a specific public dataset) for training data. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or specific computing environments) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Training is performed using Adam [7]' but does not provide version numbers for Adam or any other software dependencies. |
| Experiment Setup | Yes | For each of these tasks, we perform a grid search on the loss parameters and on our hyper-parameters. Training is performed using Adam [7] and success rates are obtained by running the optimisation with 100 different random seeds. |