Damian Podareanu

TL;DR: Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories.

TL;DR: The challenges and novel solutions needed in order to train ResNet-50 in this large scale environment are described and the novel Collapsed Ensemble (CE) technique is introduced that allows for a 77.5\% top-1 accuracy, similar to that of a Res net-152, while training a unmodified Res Net-50 topology for the same fixed training budget.

TL;DR: NetSquid as discussed by the authors is a discrete event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer and its control plane up to the application level.

TL;DR: This work introduces NetSquid, a generic discrete-event based platform for simulating all aspects of quantum networks and modular quantum computing systems, ranging from the physical layer hardware and the control plane all the way to the application level, and showcases Netsquid's ability to investigate large networks.

TL;DR: In this paper , the authors proposed and evaluated an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology, which includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis.