todo.md (1305B)
1 TODO: 2 3 Structure: 4 * introduction to problem 5 * What are we trying to solve 6 * Background 7 * Coding 8 * Neural Networks 9 * Decomposition Cases 10 * Gauss Newton and Landweber 11 * Tikhonov Regularization 12 * Space Regularization 13 * symmetries 14 * Theoretical Results 15 * Gauss-Newton type method for problem 16 * Convergence of Gauss-Newton 17 * Newton's method with nn operator and linear independence 18 * Results of linear independence 19 * Experimental Results 20 * Gauss-Newton 21 * Landweber 22 * Circular NNs with result. 23 24 We need 25 * more on coding 26 * Tikhonov regularization 27 * Space regularization 28 * more on the decomposition cases 29 * Proof of local convergence of GN 30 * Proof of linear indepencdence thm 31 * Proof of GN convergence with linear independence 32 * Reproduction of numerical results of GN 33 * Reproduction of numerical results of landweber 34 * Reproduction of numerical results of circualr networks. 35 * (reproduction or more in depth explanations) 36 37 38 Proving convergence: 39 * prove Lipschitz-Differentiable immersion of shallow NNs 40 * Linear independence of the activation function, first derivative and first 41 moment of the first derrivaive 42 * Newton Minkowski conditions for shallow NNs 43 * Moore Penrose inverse 44 45 46