15 citations · 16 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Filip de Roos, Carl Jidling, Adrian Wills +2
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic moti…
eess.IV2021
How Convolutional Neural Networks Deal with Aliasing
Antônio H. Ribeiro, Thomas B. Schön
The convolutional neural network (CNN) remains an essential tool in solving computer vision problems. Standard convolutional architectures consist of stacked layers of operations t…
stat.CO2015★ 15 cited
Particle ancestor sampling for near-degenerate or intractable state transition models
Fredrik Lindsten, Pete Bunch, Sumeetpal S. Singh +1
We consider Bayesian inference in sequential latent variable models in general, and in nonlinear state space models in particular (i.e., state smoothing). We work with sequential M…