Search Results for author:
Found 11 papers, 10 papers with code
REFORMS: Reporting Standards for Machine Learning Based Science
Machine learning (ML) methods are proliferating in scientific research.
Perfectly predicting ICU length of stay: too good to be true
A paper of Alsinglawi et al was recently accepted and published in Scientific Reports.
Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models.
Ranked #1 on
Multimodal Sleep Stage Detection
on Sleep-EDF-SC
Automatic Sleep Stage Classification
BIG-bench Machine Learning
+2
pyRDF2Vec: A Python Implementation and Extension of RDF2Vec
This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions.
R-GCN: The R Could Stand for Random
The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs).
Ranked #1 on
Node Classification
on MUTAG
Deep learning models for predicting RNA degradation via dual crowdsourcing
Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models.
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques.
Overly Optimistic Prediction Results on Imbalanced Data: a Case Study of Flaws and Benefits when Applying Over-sampling
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth.
GENDIS: GENetic DIscovery of Shapelets
It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets.
Ranked #3 on
Outlier Detection
on ECG5000
Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph
Deep-learning based techniques are increasingly being used for different machine learning tasks on knowledge graphs.
Ranked #3 on
Node Classification
on BGS
GENESIM: genetic extraction of a single, interpretable model
The results show that GENESIM achieves a better predictive performance on most of these data sets than decision tree induction techniques and a predictive performance in the same order of magnitude as the ensemble techniques.
