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This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans?

In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teachingfocuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner'snew state.

Neural Information Processing Systems http://nips.cc/

Weprovide visualization results onMNIST and CIFAR toshowour uncertainty learning method also works well on datasets besides facial expression recognition (FER) tasks. Weutilize red rectangles to mark images that are misclassified and green rectangles to mark images that are rightly classified. They are usually very hard to be rightlyclassified. We also carry out experiments on MNIST and CIFAR with synthetic noises. If the maximum prediction probability is higher than the one of given label with a threshold (set to 0.2), we believe that sample contains label noise and then change the label to the index of the maximum prediction probability.

Figure 1 illustrates model predictions across every Number Game concept in [33].Figure 6: Model predictions across every Number Game concept in [33] (Figure 1). For the number game, every model has its outputs transformed by a learned Platt transform. Logical concept models do not use Platt transforms. We fit these parameters using Adam with a learning rate of 0.001. For the number game we do 10-fold cross validation to calculate holdout predictions.