2. Wissenschaftspreis AIC 2021

Generalization, Overfitting, Aic

First some context. In supervised learning in general, the goal is to learn or infer an (initially) unknown function f : x 7→ y, from a set of training data in the form of T “input/output” pairs {(xμ, yμ)}μ=1:T . 1 More generally, you try to infer the conditional distribution ρ(y|x) from this training set; the reason is that in general your outputs contains some noise (or stated better, trial-t...

Factor Analysis and Aic

The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis. The use of the AIC criterion in the factor analysis is particularly interesting whe...

EMBL Programme 2017-2021: Digital Biology

Structure of carbohydrate of hemoglobin AIc.

Hemoglobin AIc is a minor component of normal adult erythrocytes whose concentration is elevated approximately 2-fold in patients with diabetes mellitus. Previous work suggested that the unique structural feature of hemoglobin AIc is the presence of a low molecular weight sugar moiety at the NH2-terminal valine of the beta chain. In this study the structure of the carbohydrate moiety and the na...

Stabilizing Image Mosaicing by the Geometric Aic

The computation for image mosaicing using homographies is numerically unstable and causes large image distortions if the matching points are small in number and concentrated in a small region in each image. This instability stems from the fact that actual transformations of images are usually in a small subgroup of the group of homographies. In this paper, we show that such undesirable distorti...