387 citations · 592 across the 31 of their papers we have counts for
6 papers · 1 filter
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Expl…
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpreta…
BIAS: Transparent reporting of biomedical image analysis challenges
Lena Maier-Hein, Annika Reinke, Michal Kozubek +11
The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common…
Deep Learning architectures for generalized immunofluorescence based nuclear image segmentation
Florian Kromp, Lukas Fischer, Eva Bozsaky +5
Separating and labeling each instance of a nucleus (instance-aware segmentation) is the key challenge in segmenting single cell nuclei on fluorescence microscopy images. Deep Neura…
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects
Sebastian Hofstätter, Allan Hanbury
Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems. The time required to pre…
On the Effect of Low-Frequency Terms on Neural-IR Models
Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff +1
Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words. Whil…