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53![1 Proof of Theorem 1 Theorem 1. Suppose K + 1 distributions pk are linearly spaced along a path γ. Assuming perfect transitions, if θ(β) and the Fisher information matrix Gθ (β) = covx∼pθ (∇θ log pθ (x)) are 1 Proof of Theorem 1 Theorem 1. Suppose K + 1 distributions pk are linearly spaced along a path γ. Assuming perfect transitions, if θ(β) and the Fisher information matrix Gθ (β) = covx∼pθ (∇θ log pθ (x)) are](https://www.pdfsearch.io/img/f18a6fd05537d2c71976ab7cee14bc0a.jpg) | Add to Reading ListSource URL: www.cs.toronto.eduLanguage: English - Date: 2015-06-03 15:15:50
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60![Appendices for the ICML paper “Optimizing Neural Networks with Kronecker-factored Approximate Curvature” A Appendices for the ICML paper “Optimizing Neural Networks with Kronecker-factored Approximate Curvature” A](https://www.pdfsearch.io/img/682a0c36d8807ec8e6fe44b118e163e6.jpg) | Add to Reading ListSource URL: www.cs.toronto.eduLanguage: English - Date: 2015-06-03 15:15:50
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