Michael Bohlke-Schneider
Michael Bohlke-Schneider
Machine Learning Manager at Amazon
Verified email at
Cited by
Cited by
Gluonts: Probabilistic and neural time series modeling in python
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
Journal of Machine Learning Research 21 (116), 1-6, 2020
High-dimensional multivariate forecasting with low-rank gaussian copula processes
D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus
Advances in neural information processing systems 32, 2019
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 2020
An integrated workflow for crosslinking mass spectrometry
ML Mendes, L Fischer, ZA Chen, M Barbon, FJ O'Reilly, SH Giese, ...
Molecular systems biology 15 (9), e8994, 2019
Deep learning for time series forecasting: Tutorial and literature survey
K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ...
ACM Computing Surveys 55 (6), 1-36, 2022
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240 6, 2020
Normalizing kalman filters for multivariate time series analysis
E de Bézenac, SS Rangapuram, K Benidis, M Bohlke-Schneider, R Kurle, ...
Advances in Neural Information Processing Systems 33, 2995-3007, 2020
Protein tertiary structure by crosslinking/mass spectrometry
M Schneider, A Belsom, J Rappsilber
Trends in biochemical sciences 43 (3), 157-169, 2018
Serum albumin domain structures in human blood serum by mass spectrometry and computational biology
A Belsom, M Schneider, L Fischer, O Brock, J Rappsilber
Molecular & Cellular Proteomics 15 (3), 1105-1116, 2016
X‐ray vs. NMR structures as templates for computational protein design
M Schneider, X Fu, AE Keating
Proteins: Structure, Function, and Bioinformatics 77 (1), 97-110, 2009
In situ structural restraints from cross-linking mass spectrometry in human mitochondria
PSJ Ryl, M Bohlke-Schneider, S Lenz, L Fischer, L Budzinski, M Stuiver, ...
Journal of Proteome Research 19 (1), 327-336, 2019
PSA-GAN: Progressive self attention GANs for synthetic time series
P Jeha, M Bohlke-Schneider, P Mercado, S Kapoor, RS Nirwan, ...
The Tenth International Conference on Learning Representations, 2022
Combining physicochemical and evolutionary information for protein contact prediction
M Schneider, O Brock
PloS one 9 (10), e108438, 2014
EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction
K Stahl, M Schneider, O Brock
BMC bioinformatics 18, 1-11, 2017
Blind testing of cross‐linking/mass spectrometry hybrid methods in CASP11
M Schneider, A Belsom, J Rappsilber, O Brock
Proteins: Structure, Function, and Bioinformatics 84, 152-163, 2016
Chronos: Learning the language of time series
AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ...
arXiv preprint arXiv:2403.07815, 2024
RBO Aleph: leveraging novel information sources for protein structure prediction
M Mabrouk, I Putz, T Werner, M Schneider, M Neeb, P Bartels, O Brock
Nucleic acids research 43 (W1), W343-W348, 2015
Blind evaluation of hybrid protein structure analysis methods based on cross-linking
A Belsom, M Schneider, O Brock, J Rappsilber
Trends in biochemical sciences 41 (7), 564-567, 2016
The structure of active opsin as a basis for identification of GPCR agonists by dynamic homology modelling and virtual screening assays
M Schneider, S Wolf, J Schlitter, K Gerwert
FEBS letters 585 (22), 3587-3592, 2011
Resilient neural forecasting systems
M Bohlke-Schneider, S Kapoor, T Januschowski
Proceedings of the Fourth International Workshop on Data Management for End …, 2020
The system can't perform the operation now. Try again later.
Articles 1–20