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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method
Authors: Simiao Ren, Willie Padilla, Jordan Malof
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using this metric, we compare several state-of-the-art inverse modeling approaches on four benchmark tasks: two existing tasks, a new 2-dimensional sinusoid task, and a challenging modern task of meta-material design. |
| Researcher Affiliation | Academia | Dept. of Electrical and Computer Engineering Duke University Durham, NC 27705 |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We release code for all inverse models, as well as (fast) simulation software for each benchmark problem, so that other researchers can easily repeat our experiments. 1https://github.com/Benson Ren/BDIMNNA |
| Open Datasets | Yes | Inspired by the recent benchmark study [2], we include two popular existing tasks: ballistics targeting (D1), and robotic arm control (D3). For these two tasks we use the same experimental designs as [2], including their simulator (i.e., forward model) parameters, simulator sampling procedures, and their training/testing splits. |
| Dataset Splits | Yes | For these two tasks we use the same experimental designs as [2], including their simulator (i.e., forward model) parameters, simulator sampling procedures, and their training/testing splits. All details can be found in [2] and our supplement. |
| Hardware Specification | No | The paper mentions 'common hardware' but does not provide specific details like GPU or CPU models. |
| Software Dependencies | No | The paper mentions using 'modern deep learning software packages' but does not list specific software names with version numbers. |
| Experiment Setup | No | All models utilized the same training and testing data, batch size, and stopping criteria (for training). In those cases where model hyperparameters were not available from [2], we budgeted approximately one day of computation time (on common hardware) to optimize hyperparameters, while again constraining model sizes. Full implementation details can be found in the supplementary material. |