Biblio
]
(2023). Bayesian Calibration of MEMS Accelerometers.
IEEE Sensors Journal.
(2023). Deep transformation models for functional outcome prediction after acute ischemic stroke.
Biometrical Journal. 65, 2100379.
(2023). Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.
Global Spine Journal. 21925682231154543.
(2023). Short-term density forecasting of low-voltage load using Bernstein-polynomial normalizing flows.
IEEE Transactions on Smart Grid.
(2022). 140. Automated measurement technique for coronal parameters using a novel artificial intelligence algorithm: an independent validation study on 100 preoperative AP spine X-rays.
The Spine Journal. 22, S74.
(2022). Deep and interpretable regression models for ordinal outcomes.
Pattern Recognition. 122, 108263.
(2022). Validation study of an algorithm based on artificial intelligence for automated computation of coronal parameters on preoperative AP X-rays.
Brain and Spine. 2, 101156.
(2021). Accelerating Active Learning Image Labeling Through Bulk Shift Recommendations.
2021 International Conference on Data Mining Workshops (ICDMW). 398–404.
(2021). Deep transformation models: Tackling complex regression problems with neural network based transformation models.
Accepted for Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan/Online, 2021.
(2021). Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows.
ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual.
(2020). Integrating uncertainty in deep neural networks for MRI based stroke analysis.
Medical Image Analysis. 65, 101790.
(2019). Beyond ImageNet: Deep Learning in Industrial Practice.
Applied Data Science. 205-232.
(2018). Automatic classification of non-small cell lung cancer histologic sub-types by deep learning.
VIRCHOWS ARCHIV. 108-108.
(2018). Capturing Suprasegmental Features of a Voice with RNNs for Improved Speaker Clustering.
IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 333–345.
ANNPR_2018b.pdf (692.47 KB)
ANNPR_2018b.pdf (692.47 KB)
(2018). Developing deep learning applications for life science and pharma industry.
Drug research. 68, 305–310.
(2018). Know When You Don't Know: A Robust Deep Learning Approach in the Presence of Unknown Phenotypes.
Assay and drug development technologies. 16, 343–349. adt.2018.859.pdf (711.06 KB)
adt.2018.859.pdf (711.06 KB)
(2018). Learning Neural Models for End-to-End Clustering.
IAPR Workshop on Artificial Neural Networks in Pattern Recognition. 126–138. ANNPR_2018a.pdf (3.43 MB)
ANNPR_2018a.pdf (3.43 MB)
(2017). Learning embeddings for speaker clustering based on voice equality.
Machine Learning for Signal Processing (MLSP), 2017 IEEE 27th International Workshop on. 1–6. MLSP_2017.pdf (1.34 MB)
MLSP_2017.pdf (1.34 MB)
(2016). Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.
Journal of biomolecular screening. 21, 998–1003.
(2016). Speaker Identification and Clustering using Convolution Neural Networks.
IEEE International workshop on Machine Learning for Signal Processing.
(2015). Deep Learning on a Raspberry Pi for Real Time Face Recognition..
Eurographics (Posters). 11–12.
(2015). Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial).
Clinical Cancer Research. clincanres––3135.
(2014). JOINT\_FORCES: Unite Competing Sentiment Classifiers with Random Forest..
SemEval@ COLING. 366–369.