3 citations · 3 across the 3 of their papers we have counts for
8 papers
Explaining Classifiers by Constructing Familiar Concepts
Johannes Schneider, Michail Vlachos
Interpreting a large number of neurons in deep learning is difficult. Our proposed `CLAssifier-DECoder' architecture (ClaDec) facilitates the understanding of the output of an arbi…
Personalization of Deep Learning
Johannes Schneider, Michail Vlachos
We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to…
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration
Kubilay Atasu, Thomas Parnell, Celestine Dünner +6
The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents…
Topic Modeling based on Keywords and Context
Johannes Schneider
Current topic models often suffer from discovering topics not matching human intuition, unnatural switching of topics within documents and high computational demands. We address th…
Scalable and interpretable product recommendations via overlapping co-clustering
Reinhard Heckel, Michail Vlachos, Thomas Parnell +1
We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our…
Compressive Mining: Fast and Optimal Data Mining in the Compressed Domain
Michail Vlachos, Nikolaos Freris, Anastasios Kyrillidis
Real-world data typically contain repeated and periodic patterns. This suggests that they can be effectively represented and compressed using only a few coefficients of an appropri…