Tags: ml

State: None

Hypothesis: some conditions should be true for a randomly initialized network to learn effectively:

- Initialization => should be unbiased w.r.t data used a. Either pick a better initialization scheme, or b. Perform semi-supervised learning technique to minimize entropy ( #todo cite here) - http://www.iro.umontreal.ca/~lisa/pointeurs/semi-supervised-entropy-nips2004.pdf
- A randomly initialized architecture should map each input to a "different mapping" for each input - as it is unbiased

# [[id:1415410F-EBC0-4C34-89E8-4B1A2EDEC428][Rethinking the value of network pruning]]

Xavier http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf

Learning Discrete Representations via Information Maximizing Self-Augmented Training https://arxiv.org/pdf/1702.08720.pdf

- Weight Agnostic Neural Networks https://arxiv.org/pdf/1906.04358.pdf
- Some-what of a contradiction to the [[id:0B555BDF-556C-46E5-B98A-1C46936E3367][Lottery Ticket Hypothesis (LTH)]]
- Well not quite but it is close