Position: Application-Driven Innovation in Machine Learning
Authors: David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. |
| Researcher Affiliation | Academia | 1Mc Gill University and Mila Quebec AI Institute, Montreal, Canada 2University of Toronto and Vector Institute, Toronto, Canada 3Massachusetts Institute of Technology, Cambridge, USA 4University of Southern California, Los Angeles, USA 5Arizona State University, Tempe, USA 6Inria Paris, Paris, France 7University of Colorado Boulder, Boulder, USA 8Harvard University, Cambridge, USA 9University of Alberta and Alberta Machine Intelligence Institute, Edmonton, Canada. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | This is a position paper discussing research paradigms and does not present novel code for release. Therefore, it does not provide concrete access to source code for a methodology described within this paper. |
| Open Datasets | No | The paper discusses various datasets as examples in the context of methods-driven and application-driven research, but it does not present its own experimental work or provide access information for a dataset used for training by the authors of this paper. |
| Dataset Splits | No | The paper is a position paper and does not describe its own experiments or provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | This paper is a position paper and does not describe experiments conducted by the authors. Therefore, it does not provide hardware specifications used for running experiments. |
| Software Dependencies | No | This paper is a position paper and does not describe experiments conducted by the authors. Therefore, it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | This paper is a position paper and does not describe experiments conducted by the authors. Therefore, it does not provide specific experimental setup details, hyperparameters, or training configurations. |