Post Selections Using Test Sets (PSUTS) and How Developmental Networks Avoid Them

For example, a "what" concept is "where"-invariant and a "where" concept is "what"-invariant, as explained in [55], [68]. Section IV discusses an optimal framework through which such abstractions can take place from learning simple rules during early life that enable learning of more complex rules during later life-- called scaffolding [69]. Theorem 2 leads to two observations on data fitting on a static data set: Observation 1: Any data fitting on a static data set without learning invariant concepts are nonscalable, including the n-fold cross-validation discussed below. Unfortunately, data fitting on a static data set is a norm in all ImageNet Contests [66]. Namely, the remaining subsections in this section analyze approaches that are nonscalable. For example, computer vision is not a "one-shot" pattern classification problem as argued by Li Fei-Fei et al. [19] (which was questioned in PubMed without responses), but rather a spatiotemporal problem to learn various invariant concepts present in cluttered natural scenes through autonomous attention saccades, as explained further in Observation 2. Observation 2: Learning invariant concepts seem nonscalable for any data fitting on a static data set either, because there are too many images to be labeled by hand (e.g., all pixel locations) [55], [68]. Like a human baby, any scalable machine learning methods must be conscious through which the machine learner must consciously guess concepts (i.e., not just active learning [70]) (e.g., an object type) and verify their invariance rules (e.g., the where-invariance of a what concept).