In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its use is more costly than the original problem solving from which it was learned.
We present an approach for planning robotic manipulation tasks that uses a learned mapping between geometric states and logical predicates.
On top of using a learned dictionary made of two PCA bases, PLE employs the concepts of block sparsity and some weights capturing the probability of an atom being used.
Yin et al. [31] exploited the hierarchical structure in document images and presented a bottom-up grouping method: CCs of an input image were grouped into textlines by using a learned metric.
The advantage of using a learned dictionary is that the representation would be more accurate than obtained with the use of fixed basis and this is done by adapting its atoms to fit a given training data [45].
Figures 3, 4 and 5 show the average percent of all control tests that reached 50,000 rounds for each network type using a learned policy and 3, 5 and 10 controllers, respectively.
Next, we removed unreliable lowly expressed transcripts by using a learned read coverage threshold similar to previous study [ 28].
We trained monkeys to locate targets by either following head gaze or using a learned association of face identity with the same targets.
Independently, the same whole genome alignment was analysed using BRATNextGen [ 55], assuming three clusters, using a learned value of alpha, a window size of one kilobase and a significance threshold P value of 0.05 (as calculated from 100 permutations).
The same alignment was also analyzed with BRATNextGen (Marttinen et al. 2012), assuming four clusters, using a learned value of alpha, a window size of 1 kb, and a significance threshold P value of 0.05 (as calculated from 100 permutations).
