Children are remarkably fast in learning new skills; How do they do this?

The problem of extracting auseful interpretation from an image may be simplified byimagesegmentation, i.e., by partitioning such image into regions associated to separate objects.

Live bats demonstrate that the properly used ultrasound can be used to perceive 3D space.

Efficient coding theory hasbeen used topredict howthe brain should allocate its limited resources in these scenarios by removing redundant information [1-4].

Given the rich literature on random matrices, it is not surprising to find that the rank of the intermediate representations in unnormalized networks collapses quickly with depth.

In machine learning, given a finite set of samples, there are usually multiple solutions that can perfectly fit the training data, but theinductive biasof a learning algorithm selects and prioritizes those solutions that agree with itsaprioriassumptions [1,2].

We wish to optimize the likelihood of the sequence conditioned on the start and the goal frame p(o2:T 1|o1,T).

Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples.

Moregenerally,weshowthat anyequivariant map for the hierarchy has this form.

As a remark, using the Krylov-Bogoliubov existence theorem (see Corollary 11.8 of [6]), fixed points to(4)exist as long as one can show{ρt,t 0}istight. The learning rate is set differently foreachtask. Obviously, the HV indicator (Eq.(10)) can also be used as an objective function for optimizing solution sets. For example, [25, 7] greedily add new points to obtain the highest expected HV improvement. However, the landscape of the HV indicator is piece-wise constant (similar to the 0-1 loss in classification) and is difficult to optimize with gradient descent. Particularly, for all the dominated points inthe solution set, their gradient iszero.