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..
Learning with Algorithmic Supervision via Continuous Relaxations
Authors: Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed continuous relaxation model on four challenging tasks and show that it can keep up with relaxations specifically designed for each individual task. |
| Researcher Affiliation | Collaboration | Felix Petersen University of Konstanz EMAIL Christian Borgelt University of Salzburg EMAIL Hilde Kuehne University of Frankfurt MIT-IBM Watson AI Lab EMAIL Oliver Deussen University of Konstanz EMAIL |
| Pseudocode | No | Pseudo-code for all algorithms as well as additional information on the relaxation and the inverse temperature parameter can be found in the supplementary material. |
| Open Source Code | Yes | The implementation of this work including a high-level Py Torch [36] library for automatic continuous relaxation of algorithms is openly available at github.com/Felix-Petersen/algovision. |
| Open Datasets | Yes | a set of four-digit numbers based on concatenated MNIST digits [29] is given...data set of 13 object classes from Shape Net [30]...Warcraft terrains...EMNIST data set [12] |
| Dataset Splits | No | For each task, we tune this parameter on a validation set. |
| Hardware Specification | No | The paper mentions general hardware terms like 'GPUs' but does not provide specific details such as model numbers, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions 'Py Torch [36]' and 'Adam optimizer [38]' but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | Yes | For training, we use the Adam optimizer [38] with a learning rate of 10 4 for between 1.5 105 and 1.5 106 iterations...train for 50 epochs with a batch size of 70...maximum batch size of 2...For training, we use an inverse temperature of β = 9 and Adam (η = 10 4) for 128 512 iterations. |