https://arxiv.org/abs/2202.05780 close this message arXiv smileybones icon Global Survey In just 3 minutes, help us better understand how you perceive arXiv. Take the survey TAKE SURVEY Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation and member institutions. arxiv logo > cs > arXiv:2202.05780 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2202.05780 (cs) [Submitted on 11 Feb 2022] Title:A Modern Self-Referential Weight Matrix That Learns to Modify Itself Authors:Kazuki Irie, Imanol Schlag, Robert Csordas, Jurgen Schmidhuber Download PDF Abstract: The weight matrix (WM) of a neural network (NN) is its program. The programs of many traditional NNs are learned through gradient descent in some error function, then remain fixed. The WM of a self-referential NN, however, can keep rapidly modifying all of itself during runtime. In principle, such NNs can meta-learn to learn, and meta-meta-learn to meta-learn to learn, and so on, in the sense of recursive self-improvement. While NN architectures potentially capable of implementing such behavior have been proposed since the '90s, there have been few if any practical studies. Here we revisit such NNs, building upon recent successes of fast weight programmers and closely related linear Transformers. We propose a scalable self-referential WM (SRWM) that uses outer products and the delta update rule to modify itself. We evaluate our SRWM in supervised few-shot learning and in multi-task reinforcement learning with procedurally generated game environments. Our experiments demonstrate both practical applicability and competitive performance of the proposed SRWM. Our code is public. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2202.05780 [cs.LG] (or arXiv:2202.05780v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2202.05780 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kazuki Irie [view email] [v1] Fri, 11 Feb 2022 17:24:31 UTC (9,234 KB) Full-text links: Download: * PDF * Other formats [by-4] Current browse context: cs.LG < prev | next > new | recent | 2202 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export bibtex citation Loading... Bibtex formatted citation x [loading... ] Data provided by: Bookmark BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo (*) Bibliographic Tools Bibliographic and Citation Tools [ ] Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) [ ] Litmaps Toggle Litmaps (What is Litmaps?) [ ] scite.ai Toggle scite Smart Citations (What are Smart Citations?) ( ) Code & Data Code and Data Associated with this Article [ ] arXiv Links to Code Toggle arXiv Links to Code & Data (What is Links to Code & Data?) ( ) Demos Demos [ ] Replicate Toggle Replicate (What is Replicate?) ( ) Related Papers Recommenders and Search Tools [ ] Connected Papers Toggle Connected Papers (What is Connected Papers?) [ ] Core recommender toggle CORE Recommender (What is CORE?) ( ) About arXivLabs 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 and how to get involved. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack