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Found 5 papers, 0 papers with code
On "Deep Learning" Misconduct
This paper establishes a theorem that a simple method called Pure-Guess Nearest Neighbor (PGNN) reaches any required errors on validation data set and test data set, including zero-error requirements, through the same misconduct, as long as the test data set is in the possession of the authors and both the amount of storage space and the time of training are finite but unbounded.
Why Deep Learning's Performance Data Are Misleading
A theorem is established that the NNWT method reaches a zero error on any validation set and any test set using the two misconducts, as long as the test set is in the possession of the author and both the amount of storage space and the time of training are finite but unbounded like with many deep learning methods.
Developmental Network Two, Its Optimality, and Emergent Turing Machines
By dynamic, we mean the automatic selection of features while disregarding distractors is not static, but instead based on dynamic statistics (e. g. because of the instability of shadows in the context of landmark).
Post-Selections in AI and How to Avoid Them
To avoid future pitfalls in AI competitions, this paper proposes a new AI metrics, called developmental errors for all networks trained, under Three Learning Conditions: (1) an incremental learning architecture (due to a "big data" flaw), (2) a training experience and (3) a limited amount of computational resources.
A Model for Auto-Programming for General Purposes
This theoretical work shows how the Developmental Network (DN) can accomplish this.
