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TaskMet: Task-Driven Metric Learning for Model Learning
We propose take the task loss signal one level deeper than the parameters of the model and use it to learn the parameters of the loss function the model is trained on, which can be done by learning a metric in the prediction space.
$f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception
While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as self-driving vehicles.
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