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This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and robustness of cautious (set-valued) predictions targeting multiple utilities, which may involve reward-sensitivity. To mitigate the training and storage burdens, we pr...
This paper describes formally how MLC can be transformed into an order structure learning and prediction task, and then proceeds to study the problem of predicting Bayes-optimal order structures.
A multimodal deep learning framework that integrates historical taxi Global Positioning System trajectories, metro passenger flows, and categorical spatial identifiers that is the most accurate at every tested history length at the hourly resolution and competitive with the strongest baselines at the daily resolution i...
It is shown that the integration of existing models of argumentation dialogues helps to create conversational agents that can guide non-expert users to receive system-centred XAI explanations in a way that expert users do.
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