Scenario II Sample Clauses

Scenario II. A Non-Exempt employee receives a call from any supervisor at a conference that he or she is attending on behalf of the college to assist in handling an emergency. The call is received in the evening at which time the employee is not responsible for attending any sessions. The employee and the supervisor are on the phone for 30 minutes and it is determined that the employee cannot help resolve the problem. The employee is compensated for 30 minutes of overtime pay, which is to be indicated on his or her timesheet as overtime. Scenario III

Scenario II. The probability that label y is corrupted to some other ^ In the second scenario, for each i-th sample we are given a unique label y˜i that is produced by aggregating the H indi- vidual labels according to some known aggregating policy (like majority vote). In this case, since we don’t have access to the individual annotations we assume that M is provided. i ∈ | label y˜i is given by the aggregated noise transition matrix Γ [0, 1]C×C, where Γij := P(y˜ = j y = i) is the proba- bility of the true label i being flipped into a corrupted label j and C is the number of classes. Note that by definition Γ is a right stochastic matrix that is determined by T , the amount of annotators H and the aggregating policy. We will study both the case where Γ = T , and the case in which there exists a generic Lipschitz function ϕ so that Γ−1 = ϕ(T ). sample y˜i = yi we have Γ = Ta. Or if annotators are There are different policy choices to construct the dataset that lead to Γ = T . If we decide to use only one annotator, for instance a, to build the final dataset, namely for each of D and M .