[1410.4615] Learning to Execute

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. arXiv Operational Status Get status notifications via email or slack

[1410.4615] Learning to Execute Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Neural and Evolutionary Computing arXiv:1410.4615 (cs) [Submitted on 17 Oct 2014 ( v1 ), last revised 19 Feb 2015 (this version, v3)] Title: Learning to Execute Authors: Wojciech Zaremba , Ilya Sutskever View a PDF of the paper titled Learning to Execute, by Wojciech Zaremba and 1 other authors View PDF Abstract: Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are widely used b

related reading

• The Unreasonable Effectiveness of Recurrent Neural Networkskarpathy.github.io
• Aman's AI Journal • Primers • Ilya Sutskever's Top 30aman.ai
• Composer2.pdfcursor.com
• [1409.3215] Sequence to Sequence Learning with Neural Networksarxiv.org
• GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculum · GitHubgithub.com
• Understanding LSTM Networks -- colah's blogcolah.github.io
• The Unreasonable Effectiveness of Recurrent Neural Networkskarpathy.github.io
• Long short-term memory - Wikipediaen.wikipedia.org
• Andrej Karpathy — AGI is still a decade awaydwarkesh.com
• NL.pdfabehrouz.github.io
• GenAI Handbookgenai-handbook.github.io
• Continuous Thought Machinespub.sakana.ai