Attempted Blind Constrained Descent Experiments

02/18/2021 ∙ by Prasad N R, et al. ∙ 0

Blind Descent uses constrained but, guided approach to learn the weights. The probability density function is non-zero in the infinite space of the dimension (case in point: Gaussians and normal probability distribution functions). In Blind Descent paper, some of the implicit ideas involving layer by layer training and filter by filter training (with different batch sizes) were proposed as probable greedy solutions. The results of similar experiments are discussed. Octave (and proposed PyTorch variants) source code of the experiments of this paper can be found at . This is compared against the ABCDE derivatives of the original PyTorch source code of .



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