Universality of Gradient Descent Neural Network Training

Computer Science > Machine Learning Title:Universality of Gradient Descent Neural Network Training Abstract:It has been observed that design choices of neural networks are often crucial for their successful optimization. In this article, we therefore discuss the question if it is always possible to redesign a neural network so that it trains well with gradient descent. This yields the following universality result: If, for a given network, there is any algorithm that can find good network weights for a classification task, then there exists an extension of this network that reproduces these weights and the corresponding forward output by mere gradient descent training. The construction is not intended for practical computations, but it provides some orientation on the possibilities of meta-learning and related approaches. Submission history Access Paper: View PDF HTML (experimental) TeX Source Current browse context: References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography BibTeX formatted citation Bookmark Bibliographic and Citation Tools Code, Data and Media Associated with this Article Demos Recommenders and Search Tools Author Venue Institution Topic arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.