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Traub, Jonas; Steenbergen, Nikolaas; Grulich, Philipp; Rabl, Tilmann; Markl, Volker
I²: Interactive Real-Time Visualization for Streaming Data
in Proc. 20th International Conference on Extending Database Technology (EDBT), March 21-24, 2017.
March 2017
Schütt, Kristof T.; Kindermans, Pieter-Jan,; Sauceda, Huziel E.; Chiemela, Stefan; Tkatchenko, Alexandre; Müller, Klaus-Robert
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Neural Information Processing Systems (NIPS)
Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan; Müller, Klaus R.; Tkatchenko, Alexandre
Quantum-chemical insights from deep tensor neural networks
In: Nature Communications 8,
January 2017
Rohrmann, Till; Schelter, Sebastian; Rabl, Tilmann; Markl, Volker
Gilbert: Declarative Sparse Linear Algebra on Massively Parallel Dataflow Systems
in BTW 2017 (pp. 269-288)
March 2017
Renner, Thomas; Müller, Johannes; Thamsen, Lauritz; Kao, Odej
Addressing Hadoop's Small File Problem With an Appendable Archive File Format.
In the Proceedings of the Computing Frontiers Conference (CF).
Montavon, Grégoire; Samek, Wojciech; Müller, Klaus-Robert
Methods for interpreting and understanding deep neural networks
Digital Signal Processing February 2018, Vol. 73,:1-15
Kunft, Andreas; Katsifodimos, Asterios; Schelter, Sebastian; Rabl, Tilmann; Mark, Volker
BlockJoin: Efficient Matrix Partitioning Through Joins
Proceedings of the VLDB Endowment (PVLDB) Volume 10
Kiefer, Martin; Heimel, Max; Breß, Sebastian; Markl, Volker
Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models
Proceedings of the VLDB Endowment (PVLDB) Volume 10
Gupta, P.; Gramatke, A.; Einspanier, R.; Schütte, M.; von Kleist, M.; Sharbati, J.
In silico cytotoxicity assessment on cultured rat intestinal cells deduced from cellular impedance measurement.
Accepted for: Toxicology in Vitro,
Gornitz, N.; Lima, L. A.; Müller, K. R.; Kloft, M.; Nakajima, S.
Support Vector Data Descriptions and K-means Clustering: One Class?
IEEE Transactions on Neural Networks and Learning Systems, Volume: PP, Issue: 99,:1 - 13
Goldsmith, B. R.; Boley, M.; Vreeken, J.; Scheffler, M.; Ghiringhelli, L. M.
Uncovering structure-property relationships of materials by subgroup discovery.
In: New J. Phys. 19: 013031,
Giotsas, V.; Smaragdakis, G.; Feldmann, A.; Berger, A.; Aben, E.
Detecting Peering Infrastructure Outages in the Wild.
Ghiringhelli, L. M.; Vybiral, J.; Ahmetcik, E.; Ouyang, R.; Levchenko,, S. V.; Draxl, C.; Scheffler, M.
Learning physical descriptors for materials science by compressed sensing.
In: New J. Phys. 19: 023017,
Gelß, P.; Klus, S.; Matera, S.; Schütte, Ch.
Nearest-Neighbor Interaction Systems in the Tensor Train Format.
Accepted for: J. Comp. Physics,
Feldmann, Anja; Hauswirth, M.; Markl, V.
Enabling Wide Area Data Analytics with CDPPs (Collaborative Distributed Processing Pipelines.
In: IEEE Int. Conference on Distributed Computing Systems (IEEE ICDCS), Blue-Sky Ideas / Vison Track,
Deng, D.; Fernandez, R.; Abedjan, Z.; Wang, S.; Stonebraker, S.; Elmagarmid, A.; Ilyas, I.; Madden, S.; Ouzzani, M.; Tang, N.
The Data Civilizer System.
Conrad, T.; Genzel, M.; Cvetkovic, N.; Wulkow, N.; Leichtle, A.; Vybiral, J.; Kutyniok, G.; Schütte, Ch.
Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data.
In: BMC Bioinformatics, 18(160),
Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; Müller, K.-R.
Machine learning of accurate energy-conserving molecular force fields.
In: Science Advances, 3(5),
Brockherde, F.; Vogt, L.; Li, L.; Tuckerman, M. E.; Burke, K.; Müller, K. R.
Bypassing the Kohn-Sham equations with machine learning
Nature Communications, 8(1), 872.,
Bosse, S.; Maniry, D.; Müller, K. R.; Wiegand, Th.; Samek, w.
Deep neural networks for no-reference and full-reference image quality assessment
IEEE Transactions on Image Processing.
Boley, M.; Goldsmith, B.; Ghiringhelli, L.; Vreeken, J.
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery.
In: Data Mining and Knowledge Discovery 5(2017),
Boden, Ch.; Spina, A.; Rabl, T.; Markl, V.
Benchmarking Data Flow Systems for Scalable Machine Learning.
Bergen, E.; Edlich, St.
Post-Debugging in Large Scale Analytic Systems.
In: Datenbanksysteme für Business, Technologie und Web (BTW), Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) , page 65-72.
Alber, Maximilian; Zimmert, Julian; Dogan, Urun; Kloft, Marius
Distributed optimization of multi-class SVMs
PLOS ONE 12(6): e0178161,
Alber, Maximilian; Kindermans, Pieter-Jan; Schütt, Kristof T.; Müller, Klaus-Robert; Sha, Fei
An Empirical Study on The Properties of Random Bases for Kernel Methods
Advances in Neural Information Processing Systems 30 (NIPS)