Our result shed light on the difference between Adam and (stochastic) gradient descent from a theoretical perspective.

Assuming that small spatial structures are more-or-less consistent across these measurements, these methods project to a local low-rank approximation of the data [37,31].

In practice, building f and g requires the computation for wtiwtj for all i,j. B.2 Classification For the classification task with the logistic regression model, we modify the formula of logistic regression in teaching objectives to make it convenient for derivation. It also indicates that with probability at least p1, the LST teacher can achieve exponential teachability in the iteration t. In order to achieve exponential teachiability in T iterations, the sufficient condition in Eq. (22) must be satisfied in all T iterations. Then, we use a pre-trained DenseNet [65] shown in [53] to generate 1024 dim features and the confidencescoreforeachimage.

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching (MT) algorithms for online personalized education on home devices.