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 [1].
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. |