University of Amsterdam

About: University of Amsterdam is a education organization based out in Amsterdam, Noord-Holland, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 59309 authors who have published 140894 publications receiving 5984137 citations. The organization is also known as: UvA & Universiteit van Amsterdam.

Towards complete and accurate reporting of studies of diagnostic accuracy: The STARD Initiative.

Abstract: OBJECTIVE: To improve the accuracy and completeness of reporting of studies of diagnostic accuracy, to allow readers to assess the potential for bias in the study and to evaluate its generalisability. METHODS: The Standards for Reporting of Diagnostic Accuracy (STARD) steering group searched the literature to identify publications on the appropriate conduct and reporting of diagnostic studies and extracted potential items into an extensive list. Researchers, editors, and members of professional organisations shortened this list during a two-day consensus meeting with the goal of developing a checklist and a generic flow diagram for studies of diagnostic accuracy. RESULTS: The search for published guidelines regarding diagnostic research yielded 33 previously published checklists, from which we extracted a list of 75 potential items. At the consensus meeting, participants shortened the list to a 25-item checklist, using evidence, whenever available. A prototypical flow diagram provides information about th...

Group equivariant convolutional networks

Abstract: We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. G-CNNs use G-convolutions, a new type of layer that enjoys a substantially higher degree of weight sharing than regular convolution layers. G-convolutions increase the expressive capacity of the network without increasing the number of parameters. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reflections and rotations. G-CNNs achieve state of the art results on CI- FAR10 and rotated MNIST.