5 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Differentiable Implicit Layers
Andreas Look, Simona Doneva, Melih Kandemir +2
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optio…
cs.LG2020
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann, Sebastian Gerwinn, Andreas Look +2
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity…
cs.LG2019★ 5 cited
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…