Bidirectional RNN with attention models real-time user knowledge from question-response sequences to predict correctness, outperforming baselines especially for new users on a large TOEIC mobile app dataset.
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A cycle-consistent GAN generates counterfactual medical images to attribute classification decisions more comprehensively than standard saliency methods.
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Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess
Bidirectional RNN with attention models real-time user knowledge from question-response sequences to predict correctness, outperforming baselines especially for new users on a large TOEIC mobile app dataset.
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Seeing What Shouldn't Be There: Counterfactual GANs for Medical Image Attribution
A cycle-consistent GAN generates counterfactual medical images to attribute classification decisions more comprehensively than standard saliency methods.