[2207.08799] Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

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

[2207.08799] Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2207.08799 (cs) [Submitted on 18 Jul 2022 ( v1 ), last revised 15 Jan 2023 (this version, v3)] Title: Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Authors: Boaz Barak , Benjamin L. Edelman , Surbhi Goel , Sham Kakade , Eran Malach , Cyril Zhang View a PDF of the paper titled Hidde

related reading

• [2401.05566] Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingarxiv.org
• arxiv.org/pdf/1805.08522arxiv.org
• [2405.07987] The Platonic Representation Hypothesisarxiv.org
• [2201.02177] Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetsarxiv.org
• [2512.19428] Attention Is Not What You Needarxiv.org
• [2509.14786] Pre-training under infinite computearxiv.org
• [2407.02827] Convergence of Implicit Gradient Descent for Training Two-Layer Physics-Informed Neural Networksarxiv.org
• [2312.15159] Understanding the Potential of FPGA-Based Spatial Acceleration for Large Language Model Inferencearxiv.org
• [2309.01809] Are Emergent Abilities in Large Language Models just In-Context Learning?arxiv.org
• [2403.06664] Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real Systemarxiv.org
• [2504.18208v1] Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regimearxiv.org
• [2401.02721] A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODEarxiv.org