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Found 2 papers, 2 papers with code
The Peril of Popular Deep Learning Uncertainty Estimation Methods
Secondly, we show on a 2D toy example that both BNNs and MCDropout do not give high uncertainty estimates on OOD samples.
Can we learn gradients by Hamiltonian Neural Networks?
In this work, we propose a meta-learner based on ODE neural networks that learns gradients.
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