PDF] Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind's Alpha Zero
Por um escritor misterioso
Last updated 20 setembro 2024
The significant success of reverse-engineering the important accomplishments of DeepMind's Alpha Zero exemplifies the leverage that can be achieved by a concerted effort to reproduce results. The reproducibility of scientific findings are an important hallmark of quality and integrity in research. The scientific method requires hypotheses to be subjected to the most crucial tests, and for the results to be consistent across independent trials. Therefore, a publication is expected to provide sufficient information for an objective evaluation of its methods and claims. This is particularly true for research supported by public funds, where transparency of findings are a form of return on public investment. Unfortunately, many publications fall short of this mark for various reasons, including unavoidable ones such as intellectual property protection and national security of the entity creating those findings. This is a particularly important and documented problem in medical research, and in machine learning. Fortunately for those seeking to overcome these difficulties, the internet makes it easier to share experiments, and allows for crowd-sourced reverse engineering. A case study of this capability in neural networks research is presented in this paper. The significant success of reverse-engineering the important accomplishments of DeepMind's Alpha Zero exemplifies the leverage that can be achieved by a concerted effort to reproduce results.
Emerging techniques in biological sciences - ScienceDirect
Machine Learning for Chemical Reactions
Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind's Alpha Zero
ICLR 2021
Research related to AI, ML, and Related Topics
2023 ICICT Proceedings, PDF, Machine Learning
Reproducibility via Crowdsourced Reverse Engineering: A Neural Network Case Study With DeepMind's Alpha Zero
Machine Learning for Chemical Reactions
v70/ICML2017_utf8.bib at gh-pages · mlresearch/v70 · GitHub
ICLR 2021
Donald WUNSCH, Mary K. Finley Missouri Distinguished Professor of ECE and Director, ACIL, Missouri University of Science and Technology, MO, Missouri S&T, Electrical and Computer Engineeing
Machine Learning for Chemical Reactions
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
NeurIPS 2023 Spotlight Posters
GitHub - youngfish42/Awesome-FL: Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
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