papers
Publications (2)
cs.LG2023
Looking at the posterior: accuracy and uncertainty of neural-network predictions
H. Linander, O. Balabanov, H. Yang +1
Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posteri…
cond-mat.dis-nn2023
Finite-time Lyapunov exponents of deep neural networks
L. Storm, H. Linander, J. Bec +2
We compute how small input perturbations affect the output of deep neural networks, exploring an analogy between deep networks and dynamical systems, where the growth or decay of l…