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..
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Authors: Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an error decomposition of systems with multiple machine-learning models, which sheds light on the types of errors that can lead to self-defeating improvements. We also present the results of experiments which show that self-defeating improvements emerge in a realistic stereo-based detection system for cars and pedestrians. |
| Researcher Affiliation | Collaboration | Ruihan Wu Cornell University EMAIL Chuan Guo Awni Hannun Laurens van der Maaten Facebook AI Research EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We evaluate our system on the KITTI dataset [16, CC BY-NC-SA 3.0], using the training-validation split of [12]. |
| Dataset Splits | Yes | We evaluate our system on the KITTI dataset [16, CC BY-NC-SA 3.0], using the training-validation split of [12]. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like PSMNet, Point-RCNN, and Frustum Point Net but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We experiment with two training loss functions: (1) the depth mean absolute error and (2) the disparity mean absolute error (MAE). Before computing r(d), we perform winsorization on the prediction u(xleft, xright): we remove all points that are higher than 1 meter in the point cloud (where the camera position is the origin). |