5 citations · 12 across the 9 of their papers we have counts for
9 papers
Pattern Sampling for Shapelet-based Time Series Classification
Atif Raza, Stefan Kramer
Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time…
Focusing Knowledge-based Graph Argument Mining via Topic Modeling
Patrick Abels, Zahra Ahmadi, Sophie Burkhardt +3
Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extrac…
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series
Antoine Garcon, Julian Vexler, Dmitry Budker +1
Deep neural networks (DNNs) are widely used in pattern-recognition tasks for which a human comprehensible, quantitative description of the data-generating process, e.g., in the for…
Deep Unsupervised Identification of Selected SNPs between Adapted Populations on Pool-seq Data
Julia Siekiera, Stefan Kramer
The exploration of selected single nucleotide polymorphisms (SNPs) to identify genetic diversity between different sequencing population pools (Pool-seq) is a fundamental task in g…
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Sophie Burkhardt, Jannis Brugger, Nicolas Wagner +3
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible sin…
Ranking Creative Language Characteristics in Small Data Scenarios
Julia Siekiera, Marius Köppel, Edwin Simpson +3
The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require…