543 citations · 612 across the 34 of their papers we have counts for
3 papers · 1 filter
Differential Bayesian Neural Nets
Andreas Look, Melih Kandemir
Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system. We propose…
Deep Active Learning with Adaptive Acquisition
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Model selection is treated as a standard performance boosting step in many machine learning applications. Once all other properties of a learning problem are fixed, the model is se…
Bayesian Evidential Deep Learning with PAC Regularization
Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has…