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Emergent properties of the local geometry of neural loss landscapes

, и . (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.

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Paul Fort

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Stanislav Laznicka

 

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Predictability and Surprise in Large Generative Models., , , , , , , , , и 20 other автор(ы). CoRR, (2022)Stiffness: A New Perspective on Generalization in Neural Networks, , , и . (2019)cite arxiv:1901.09491.The Break-Even Point on the Optimization Trajectories of Deep Neural Networks, , , , , , и . International Conference on Learning Representations, (2020)Emergent properties of the local geometry of neural loss landscapes., и . CoRR, (2019)Stiffness: A New Perspective on Generalization in Neural Networks, , и . (2019)cite arxiv:1901.09491.Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned., , , , , , , , , и 26 other автор(ы). CoRR, (2022)How many degrees of freedom do we need to train deep networks: a loss landscape perspective., , , и . CoRR, (2021)Large Scale Structure of Neural Network Loss Landscapes., и . NeurIPS, стр. 6706-6714. (2019)Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel., , , , , и . NeurIPS, (2020)The Break-Even Point on Optimization Trajectories of Deep Neural Networks., , , , , , и . ICLR, OpenReview.net, (2020)