{"slug": "on-the-architectural-complexity-of-neural-networks", "title": "On The Architectural Complexity of Neural Networks", "summary": "Researchers introduced a unified theoretical framework for analyzing and constructing deep neural networks by modeling tensor operations. The framework reveals a link between groundbreaking architectures and increases in architectural complexity over 40 years, and identifies unexplored high-complexity architectures. A dataset of over 3,000 such architectures has been released publicly.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 5 May 2026]\n\n# Title:On the Architectural Complexity of Neural Networks\n\n[View PDF](/pdf/2605.04325)\n\n[HTML (experimental)](https://arxiv.org/html/2605.04325v1)\n\nAbstract:We introduce a unified theoretical framework for the rigorous analysis and systematic construction of deep neural networks (DNNs). This framework addresses a gap in existing theory by explicitly modeling the structure of tensor operations -- lower level information that is often abstracted. Our framework enables two novel objectives: (1) analysis of the evolution of architectural complexity over deep learning history, and (2) automatic construction of novel architectures based on new types of tensor operations. Our study of DNNs introduced over the past 40 years reveals a connection between groundbreaking architectures and increases in different types of architectural complexity. Moreover, we identify several large classes of higher complexity architectures that have not yet been explored. We then collect a dataset of 3,000+ higher complexity architectures, which we publicly release at:[this https URL].\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/on-the-architectural-complexity-of-neural-networks", "canonical_source": "https://arxiv.org/abs/2605.04325", "published_at": "2026-06-16 22:15:39+00:00", "updated_at": "2026-06-16 22:30:49.678096+00:00", "lang": "en", "topics": ["neural-networks", "machine-learning"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/on-the-architectural-complexity-of-neural-networks", "markdown": "https://wpnews.pro/news/on-the-architectural-complexity-of-neural-networks.md", "text": "https://wpnews.pro/news/on-the-architectural-complexity-of-neural-networks.txt", "jsonld": "https://wpnews.pro/news/on-the-architectural-complexity-of-neural-networks.jsonld"}}