Deep learning (DL) algorithms rely on massive amounts of labeled data.

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The CF of a random variable (r.v.) is the Fourier transform of its probability distribution function (PDF).

RL that utilize function approximation to generalize observational data to unknown states/actions. The goal of this paper is to study the sample complexity of policy-based RL, which is arguably the simplest setting for RL with function approximation (Kearns et al., 1999; Kakade, 2003).

Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis.

Nevertheless, in somesettings,suchassocialnetworks,infectiousdiseasemodeling,andreasoning with spatial and temporal data, this assumption is false.

In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? Webe present thenotion Assume Gisagroup.Gactson and Y : G Y !

These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation.