output
19952026
most citedA consensus-based model for global optimization and its mean-field limit

146 citations

Showing 2019Show all

7 papers · 1 filter

cs.CR2019★ 1 cited

Non-Interactive Private Decision Tree Evaluation

Anselme Tueno, Yordan Boev, Florian Kerschbaum

Decision trees are a powerful prediction model with many applications in statistics, data mining, and machine learning. In some settings, the model and the data to be classified ma…

cs.CR2019★ 4 cited

Secure Computation of the kth-Ranked Element in a Star Network

Anselme Tueno, Florian Kerschbaum, Stefan Katzenbeisser +2

We consider the problem of securely computing the kth-ranked element in a sequence of n private integers distributed among n parties. The kth-ranked element (e.g., minimum, maximum…

eess.IV2019★ 5 cited

Uncertainty-Driven Semantic Segmentation through Human-Machine Collaborative Learning

Mahdyar Ravanbakhsh, Tassilo Klein, Kayhan Batmanghelich +1

Deep learning-based approaches achieve state-of-the-art performance in the majority of image segmentation benchmarks. However, training of such models requires a sizable amount of…

cs.DB2019★ 9 cited

On the Diversity of Memory and Storage Technologies

Ismail Oukid, Lucas Lersch

The last decade has seen tremendous developments in memory and storage technologies, starting with Flash Memory and continuing with the upcoming Storage-Class Memories. Combined wi…

cs.DB2019★ 7 cited

Persistent Buffer Management with Optimistic Consistency

Lucas Lersch, Wolfgang Lehner, Ismail Oukid

Finding the best way to leverage non-volatile memory (NVM) on modern database systems is still an open problem. The answer is far from trivial since the clear boundary between memo…

cond-mat.quant-gas2019★ 9 cited

Collisions of solitary waves in condensates beyond mean-field theory

Aparna Sreedharan, Sarthak Choudhury, Rick Mukherjee +2

Bright solitary waves in a Bose-Einstein condensate contain thousands of identical atoms held together despite their only weakly attractive contact interactions. They nonetheless b…