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..
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
Authors: Russell Stewart, Stefano Ermon
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of this approach on real world and simulated computer vision tasks. In our ο¬rst two experiments, we construct a mapping from an image to the location of an object it contains. |
| Researcher Affiliation | Academia | Russell Stewart , Stefano Ermon Department of Computer Science, Stanford University EMAIL |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link for 'Our data set 1' (footnote 1: https://github.com/russell91/labelfree) but does not explicitly state that the code for the methodology is open-source or available at this link. |
| Open Datasets | Yes | Our data set 1 is collected on a laptop webcam running at 10 frames per second (Ξt = 0.1s). [footnote 1: https://github.com/russell91/labelfree] |
| Dataset Splits | No | The paper mentions holding out 25 trajectories for evaluation, which is later referred to as the 'test images'. It does not explicitly specify a separate validation set or provide comprehensive train/validation/test dataset splits with percentages or counts for all three. |
| Hardware Specification | No | The paper mentions data collection on a 'laptop webcam' but does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for training or running the experiments. |
| Software Dependencies | No | The paper mentions using 'Tensor Flow' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Images are resized to 56 56 pixels... We use 3 Conv/Re LU/Max Pool blocks followed by 2 Fully Connected/Re LU layers with dropout probability 0.5 and a single regression output. We group trajectories into batches of size 16... We use the Adam optimizer... with a learning rate of 0.0001 and train for 4,000 iterations. |