| Year |
Citation |
Score |
| 2023 |
Dax M, Green SR, Gair J, Pürrer M, Wildberger J, Macke JH, Buonanno A, Schölkopf B. Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. Physical Review Letters. 130: 171403. PMID 37172245 DOI: 10.1103/PhysRevLett.130.171403 |
0.631 |
|
| 2021 |
Dax M, Green SR, Gair J, Macke JH, Buonanno A, Schölkopf B. Real-Time Gravitational Wave Science with Neural Posterior Estimation. Physical Review Letters. 127: 241103. PMID 34951790 DOI: 10.1103/PhysRevLett.127.241103 |
0.606 |
|
| 2020 |
Meding K, Bruijns SA, Schölkopf B, Berens P, Wichmann FA. Phenomenal Causality and Sensory Realism. I-Perception. 11: 2041669520927038. PMID 32537119 DOI: 10.1177/2041669520927038 |
0.749 |
|
| 2019 |
Mastakouri AA, Scholkopf B, Grosse-Wentrup M. Beta Power May Meditate the Effect of Gamma-TACS on Motor Performance. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2019: 5902-5908. PMID 31947193 DOI: 10.1109/EMBC.2019.8856416 |
0.621 |
|
| 2017 |
Hohmann MR, Fomina T, Jayaram V, Emde T, Just J, Synofzik M, Schölkopf B, Schöls L, Grosse-Wentrup M. Case series: Slowing alpha rhythm in late-stage ALS patients. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology. 129: 406-408. PMID 29291492 DOI: 10.1016/J.Clinph.2017.11.013 |
0.608 |
|
| 2017 |
Jayaram V, Hohmann M, Just J, Schölkopf B, Grosse-Wentrup M. Task-induced frequency modulation features for brain-computer interfacing. Journal of Neural Engineering. 14: 056015. PMID 28925374 DOI: 10.1088/1741-2552/Aa7778 |
0.624 |
|
| 2017 |
Fomina T, Weichwald S, Synofzik M, Just J, Schöls L, Schölkopf B, Grosse-Wentrup M. Absence of EEG correlates of self-referential processing depth in ALS. Plos One. 12: e0180136. PMID 28662161 DOI: 10.1371/Journal.Pone.0180136 |
0.606 |
|
| 2016 |
Fomina T, Lohmann G, Erb M, Ethofer T, Schölkopf B, Grosse-Wentrup M. Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS. Journal of Neural Engineering. 13: 066021. PMID 27841159 DOI: 10.1088/1741-2560/13/6/066021 |
0.613 |
|
| 2015 |
Fomina T, Hohmann M, Scholkopf B, Grosse-Wentrup M. Identification of the Default Mode Network with electroencephalography. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2015: 7566-7569. PMID 26738043 DOI: 10.1109/EMBC.2015.7320143 |
0.613 |
|
| 2015 |
Jayaram V, Widmann N, Forster C, Fomina T, Hohmann M, Muller Vom Hagen J, Synofzik M, Scholkopf B, Schols L, Grosse-Wentrup M. Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2015: 6979-6982. PMID 26737898 DOI: 10.1109/EMBC.2015.7319998 |
0.631 |
|
| 2015 |
Grosse-Wentrup M, Janzing D, Siegel M, Schölkopf B. Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach. Neuroimage. PMID 26518633 DOI: 10.1016/J.Neuroimage.2015.10.062 |
0.634 |
|
| 2015 |
Besserve M, Lowe SC, Logothetis NK, Schölkopf B, Panzeri S. Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer. Plos Biology. 13: e1002257. PMID 26394205 DOI: 10.1371/Journal.Pbio.1002257 |
0.717 |
|
| 2015 |
Weichwald S, Meyer T, Özdenizci O, Schölkopf B, Ball T, Grosse-Wentrup M. Causal interpretation rules for encoding and decoding models in neuroimaging. Neuroimage. 110: 48-59. PMID 25623501 DOI: 10.1016/J.Neuroimage.2015.01.036 |
0.645 |
|
| 2015 |
Küffner R, Zach N, Norel R, Hawe J, Schoenfeld D, Wang L, Li G, Fang L, Mackey L, Hardiman O, Cudkowicz M, Sherman A, Ertaylan G, Grosse-Wentrup M, Hothorn T, et al. Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression. Nature Biotechnology. 33: 51-7. PMID 25362243 DOI: 10.1038/Nbt.3051 |
0.726 |
|
| 2014 |
Grosse-Wentrup M, Schölkopf B. A brain-computer interface based on self-regulation of gamma-oscillations in the superior parietal cortex. Journal of Neural Engineering. 11: 056015. PMID 25125446 DOI: 10.1088/1741-2560/11/5/056015 |
0.618 |
|
| 2014 |
Meyer T, Peters J, Zander TO, Schölkopf B, Grosse-Wentrup M. Predicting motor learning performance from Electroencephalographic data. Journal of Neuroengineering and Rehabilitation. 11: 24. PMID 24594233 DOI: 10.1186/1743-0003-11-24 |
0.668 |
|
| 2013 |
Janzing D, Balduzzi D, Grosse-Wentrup M, Schölkopf B. Quantifying causal influences Annals of Statistics. 41: 2324-2358. DOI: 10.1214/13-Aos1145 |
0.616 |
|
| 2013 |
Besserve M, Logothetis NK, Schölkopf B. Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators Advances in Neural Information Processing Systems. |
0.702 |
|
| 2012 |
Grosse-Wentrup M, Schölkopf B. High γ-power predicts performance in sensorimotor-rhythm brain-computer interfaces. Journal of Neural Engineering. 9: 046001. PMID 22713543 DOI: 10.1088/1741-2560/9/4/046001 |
0.628 |
|
| 2011 |
Gomez-Rodriguez M, Grosse-Wentrup M, Hill J, Gharabaghi A, Scholkopf B, Peters J. Towards brain-robot interfaces in stroke rehabilitation. Ieee ... International Conference On Rehabilitation Robotics : [Proceedings]. 2011: 5975385. PMID 22275589 DOI: 10.1109/ICORR.2011.5975385 |
0.622 |
|
| 2011 |
Gomez-Rodriguez M, Peters J, Hill J, Schölkopf B, Gharabaghi A, Grosse-Wentrup M. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. Journal of Neural Engineering. 8: 036005. PMID 21474878 DOI: 10.1088/1741-2560/8/3/036005 |
0.611 |
|
| 2011 |
Grosse-Wentrup M, Schölkopf B, Hill J. Causal influence of gamma oscillations on the sensorimotor rhythm. Neuroimage. 56: 837-42. PMID 20451626 DOI: 10.1016/J.Neuroimage.2010.04.265 |
0.627 |
|
| 2011 |
Kitching T, Amara A, Gill M, Harmeling S, Heymans C, Massey R, Rowe B, Schrabback T, Voigt L, Balan S, Bernstein G, Bethge M, Bridle S, Courbin F, Gentile M, et al. Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook Annals of Applied Statistics. 5: 2231-2263. DOI: 10.1214/11-Aoas484 |
0.457 |
|
| 2011 |
Besserve M, Janzing D, Logothetis NK, Schölkopf B. Finding dependencies between frequencies with the kernel cross-spectral density Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 2080-2083. DOI: 10.1109/ICASSP.2011.5946735 |
0.698 |
|
| 2010 |
Besserve M, Schölkopf B, Logothetis NK, Panzeri S. Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. Journal of Computational Neuroscience. 29: 547-66. PMID 20396940 DOI: 10.1007/S10827-010-0236-5 |
0.722 |
|
| 2010 |
Bridle S, Balan ST, Bethge M, Gentile M, Harmeling S, Heymans C, Hirsch M, Hosseini R, Jarvis M, Kirk D, Kitching T, Kuijken K, Lewis A, Paulin-Henriksson S, Schölkopf B, et al. Results of the GREAT08 Challenge: an image analysis competition for cosmological lensing Monthly Notices of the Royal Astronomical Society. 405: 2044-2061. DOI: 10.1111/J.1365-2966.2010.16598.X |
0.416 |
|
| 2010 |
Gomez-Rodriguez M, Grosse-Wentrup M, Peters J, Naros G, Hill J, Schölkopf B, Gharabaghi A. Epidural ECoG online decoding of arm movement intention in hemiparesis Proceedings - Workshop On Brain Decoding: Pattern Recognition Challenges in Neuroimaging, Wbd 2010 - in Conjunction With Theinternational Conference On Pattern Recognition, Icpr 2010. 36-39. DOI: 10.1109/WBD.2010.17 |
0.602 |
|
| 2010 |
Gomez-Rodriguez M, Peterst J, Hin J, Schölkopf B, Gharabaghi A, Grosse-Wentrup M. Closing the sensorimotor loop: Haptic feedback facilitates decoding of arm movement imagery Conference Proceedings - Ieee International Conference On Systems, Man and Cybernetics. 121-126. DOI: 10.1109/ICSMC.2010.5642217 |
0.602 |
|
| 2009 |
Barbero A, Franz M, van Drongelen W, Dorronsoro JR, Schölkopf B, Grosse-Wentrup M. Implicit Wiener series analysis of epileptic seizure recordings. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2009: 5304-7. PMID 19963892 DOI: 10.1109/IEMBS.2009.5333080 |
0.617 |
|
| 2009 |
Kienzle W, Franz MO, Schölkopf B, Wichmann FA. Center-surround patterns emerge as optimal predictors for human saccade targets. Journal of Vision. 9: 7.1-15. PMID 19757885 DOI: 10.1167/9.5.7 |
0.743 |
|
| 2009 |
Jäkel F, Schölkopf B, Wichmann FA. Does cognitive science need kernels? Trends in Cognitive Sciences. 13: 381-8. PMID 19729333 DOI: 10.1016/j.tics.2009.06.002 |
0.755 |
|
| 2009 |
Corfield D, Schölkopf B, Vapnik V. Falsificationism and statistical learning theory: Comparing the popper and vapnik-chervonenkis dimensions Journal For General Philosophy of Science. 40: 51-58. DOI: 10.1007/S10838-009-9091-3 |
0.667 |
|
| 2009 |
Sinz FH, Chapelle O, Agarwal A, Schölkopf B. An analysis of inference with the Universum Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.639 |
|
| 2008 |
Jäkel F, Schölkopf B, Wichmann FA. Generalization and similarity in exemplar models of categorization: insights from machine learning. Psychonomic Bulletin & Review. 15: 256-71. PMID 18488638 DOI: 10.3758/PBR.15.2.256 |
0.755 |
|
| 2008 |
Macke JH, Maack N, Gupta R, Denk W, Schölkopf B, Borst A. Contour-propagation algorithms for semi-automated reconstruction of neural processes. Journal of Neuroscience Methods. 167: 349-57. PMID 17870180 DOI: 10.1016/j.jneumeth.2007.07.021 |
0.6 |
|
| 2008 |
Breuer P, Kim KI, Kienzle W, Schölkopf B, Blanz V. Automatic 3D face reconstruction from single images or video 2008 8th Ieee International Conference On Automatic Face and Gesture Recognition, Fg 2008. DOI: 10.1109/AFGR.2008.4813339 |
0.556 |
|
| 2008 |
Jäkel F, Schölkopf B, Wichmann FA. Similarity, kernels, and the triangle inequality Journal of Mathematical Psychology. 52: 297-303. DOI: 10.1016/j.jmp.2008.03.001 |
0.733 |
|
| 2007 |
Rätsch G, Sonnenburg S, Srinivasan J, Witte H, Müller KR, Sommer RJ, Schölkopf B. Improving the Caenorhabditis elegans genome annotation using machine learning. Plos Computational Biology. 3: e20. PMID 17319737 DOI: 10.1371/Journal.Pcbi.0030020 |
0.586 |
|
| 2007 |
Jäkel F, Schölkopf B, Wichmann FA. A tutorial on kernel methods for categorization Journal of Mathematical Psychology. 51: 343-358. DOI: 10.1016/j.jmp.2007.06.002 |
0.758 |
|
| 2007 |
Kienzle W, Wichmann FA, Schölkopf B, Franz MO. A nonparametric approach to bottom-up visual saliency Advances in Neural Information Processing Systems. 689-696. |
0.727 |
|
| 2007 |
Steinke F, Schölkopf B, Blanz V. Learning dense 3D correspondence Advances in Neural Information Processing Systems. 1313-1320. |
0.608 |
|
| 2007 |
Kienzle W, Schölkopf B, Wichmann FA, Franz MO. How to find interesting locations in video: A spatiotemporal interest point detector learned from human eye movements Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4713: 405-414. |
0.747 |
|
| 2006 |
Gretton A, Belitski A, Murayama Y, Schölkopf B, Logothetis N. The effect of artifacts on dependence measurement in fMRI. Magnetic Resonance Imaging. 24: 401-9. PMID 16677946 DOI: 10.1016/J.Mri.2005.12.036 |
0.436 |
|
| 2001 |
Müller KR, Mika S, Rätsch G, Tsuda K, Schölkopf B. An introduction to kernel-based learning algorithms. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 12: 181-201. PMID 18244377 DOI: 10.1109/72.914517 |
0.617 |
|
| 2000 |
Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, Müller KR. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics (Oxford, England). 16: 799-807. PMID 11108702 |
0.569 |
|
| 1999 |
Schölkopf B, Mika S, Burges CC, Knirsch P, Müller KR, Rätsch G, Smola AJ. Input space versus feature space in kernel-based methods. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1000-17. PMID 18252603 DOI: 10.1109/72.788641 |
0.568 |
|
| 1998 |
Smola AJ, Schölkopf B, Müller KR. The connection between regularization operators and support vector kernels. Neural Networks : the Official Journal of the International Neural Network Society. 11: 637-649. PMID 12662802 |
0.566 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2008 |
Hofmann T, Schölkopf B, Smola AJ. Kernel methods in machine learning Annals of Statistics. 36: 1171-1220. DOI: 10.1214/009053607000000677 |
0.255 |
|
| 2020 |
Locatello F, Bauer S, Lucic M, Rätsch G, Gelly S, Schölkopf B, Bachem O. A Commentary on the Unsupervised Learning of Disentangled Representations Proceedings of the Aaai Conference On Artificial Intelligence. 34: 13681-13684. DOI: 10.1609/AAAI.V34I09.7120 |
0.249 |
|
| 2014 |
Muelling K, Boularias A, Mohler B, Schölkopf B, Peters J. Learning strategies in table tennis using inverse reinforcement learning. Biological Cybernetics. 108: 603-19. PMID 24756167 DOI: 10.1007/S00422-014-0599-1 |
0.226 |
|
| 2015 |
Schuler C, Hirsch M, Harmeling S, Scholkopf B. Learning to Deblur. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 26415157 DOI: 10.1109/TPAMI.2015.2481418 |
0.218 |
|
| 2018 |
Zhang K, Schölkopf B, Spirtes P, Glymour C. Learning causality and causality-related learning: some recent progress. National Science Review. 5: 26-29. PMID 30034911 DOI: 10.1093/Nsr/Nwx137 |
0.217 |
|
| 2023 |
Laumann F, von Kügelgen J, Park J, Schölkopf B, Barahona M. Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. Entropy (Basel, Switzerland). 25. PMID 38136477 DOI: 10.3390/e25121597 |
0.211 |
|
| 2014 |
Muelling K, Kroemer O, Lampert CH, Schölkopf B. Movement Templates for Learning of Hitting and Batting Springer Tracts in Advanced Robotics. 97: 69-82. DOI: 10.1007/978-3-319-03194-1_3 |
0.21 |
|
| 2011 |
Luxburg Uv, Schölkopf B. Statistical Learning Theory: Models, Concepts, and Results Handbook of the History of Logic. 10: 651-706. DOI: 10.1016/B978-0-444-52936-7.50016-1 |
0.208 |
|
| 2007 |
Wu M, Schölkopf B. Transductive Classification via Local Learning Regularization Journal of Machine Learning Research. 2: 628-635. |
0.207 |
|
| 2015 |
Schölkopf B. Artificial intelligence: Learning to see and act. Nature. 518: 486-7. PMID 25719660 DOI: 10.1038/518486a |
0.207 |
|
| 2012 |
Schölkopf B, Janzing D, Peters J, Sgouritsa E, Zhang K, Mooij J. On causal and anticausal learning Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 2: 1255-1262. |
0.207 |
|
| 2013 |
Schölkopf B, Janzing D, Peters J, Sgouritsa E, Zhang K, Mooij J. Semi-supervised learning in causal and anticausal settings Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 129-141. DOI: 10.1007/978-3-642-41136-6_13 |
0.206 |
|
| 2012 |
Muandet K, Fukumizu K, Dinuzzo F, Schölkopf B. Learning from distributions via support measure machines Advances in Neural Information Processing Systems. 1: 10-18. |
0.198 |
|
| 2011 |
Martens SM, Mooij JM, Hill NJ, Farquhar J, Schölkopf B. A graphical model framework for decoding in the visual ERP-based BCI speller. Neural Computation. 23: 160-82. PMID 20964540 DOI: 10.1162/NECO_a_00066 |
0.193 |
|
| 2009 |
Gehler PV, Schölkopf B. An Introduction to Kernel Learning Algorithms Kernel Methods For Remote Sensing Data Analysis. 25-48. DOI: 10.1002/9780470748992.ch2 |
0.19 |
|
| 2023 |
Hawkins-Hooker A, Visonà G, Narendra T, Rojas-Carulla M, Schölkopf B, Schweikert G. Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning. Nature Communications. 14: 4750. PMID 37550323 DOI: 10.1038/s41467-023-40211-2 |
0.189 |
|
| 2007 |
Peters J, Schaal S, Schölkopf B. Towards machine learning of motor skills Informatik Aktuell. 138-144. |
0.188 |
|
| 2018 |
Xiao L, Heide F, Heidrich W, Scholkopf B, Hirsch M. Discriminative Transfer Learning for General Image Restoration. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. PMID 29993740 DOI: 10.1109/Tip.2018.2831925 |
0.186 |
|
| 2011 |
Bócsi B, Nguyen-Tuong D, Csató L, Schölkopf B, Peters J. Learning inverse kinematics with structured prediction Ieee International Conference On Intelligent Robots and Systems. 698-703. DOI: 10.1109/IROS.2011.6048552 |
0.186 |
|
| 2022 |
Xian RP, Stimper V, Zacharias M, Dendzik M, Dong S, Beaulieu S, Schölkopf B, Wolf M, Rettig L, Carbogno C, Bauer S, Ernstorfer R. A machine learning route between band mapping and band structure. Nature Computational Science. 3: 101-114. PMID 38177954 DOI: 10.1038/s43588-022-00382-2 |
0.183 |
|
| 2019 |
Tabibian B, Upadhyay U, De A, Zarezade A, Schölkopf B, Gomez-Rodriguez M. Enhancing human learning via spaced repetition optimization. Proceedings of the National Academy of Sciences of the United States of America. PMID 30670661 DOI: 10.1073/pnas.1815156116 |
0.183 |
|
| 2018 |
Huang B, Zhang K, Lin Y, Schölkopf B, Glymour C. Generalized Score Functions for Causal Discovery. Kdd : Proceedings. International Conference On Knowledge Discovery & Data Mining. 2018: 1551-1560. PMID 30191079 DOI: 10.1145/3219819.3220104 |
0.182 |
|
| 2008 |
Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. Support vector machines and kernels for computational biology. Plos Computational Biology. 4: e1000173. PMID 18974822 DOI: 10.1371/Journal.Pcbi.1000173 |
0.181 |
|
| 2009 |
Nguyen-Tuong D, Schölkopf B, Peters J. Sparse online model learning for robot control with support vector regression 2009 Ieee/Rsj International Conference On Intelligent Robots and Systems, Iros 2009. 3121-3126. DOI: 10.1109/IROS.2009.5354609 |
0.178 |
|
| 2012 |
Hill NJ, Schölkopf B. An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli. Journal of Neural Engineering. 9: 026011. PMID 22333135 DOI: 10.1088/1741-2560/9/2/026011 |
0.178 |
|
| 2009 |
Lee D, Hofmann M, Steinke F, Altun Y, Cahill ND, Schölkopf B. Learning similarity measure for multi-modal 3D image registration 2009 Ieee Computer Society Conference On Computer Vision and Pattern Recognition Workshops, Cvpr Workshops 2009. 186-193. DOI: 10.1109/CVPRW.2009.5206840 |
0.175 |
|
| 2017 |
Gong M, Zhang K, Schölkopf B, Glymour C, Tao D. Causal Discovery from Temporally Aggregated Time Series. Uncertainty in Artificial Intelligence : Proceedings of the ... Conference. Conference On Uncertainty in Artificial Intelligence. 2017. PMID 29899680 |
0.168 |
|
| 2004 |
Lal TN, Schröder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N, Schölkopf B. Support vector channel selection in BCI. Ieee Transactions On Bio-Medical Engineering. 51: 1003-10. PMID 15188871 DOI: 10.1109/Tbme.2004.827827 |
0.166 |
|
| 2007 |
Zhou D, Huang J, Schölkopf B. Learning with hypergraphs: Clustering, classification, and embedding Advances in Neural Information Processing Systems. 1601-1608. |
0.163 |
|
| 2023 |
Mineeva O, Danciu D, Schölkopf B, Ley RE, Rätsch G, Youngblut ND. ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning. Plos Computational Biology. 19: e1011001. PMID 37126495 DOI: 10.1371/journal.pcbi.1011001 |
0.159 |
|
| 2009 |
Martens SM, Hill NJ, Farquhar J, Schölkopf B. Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential. Journal of Neural Engineering. 6: 026003. PMID 19255462 DOI: 10.1088/1741-2560/6/2/026003 |
0.156 |
|
| 2021 |
Hepp T, Blum D, Armanious K, Schölkopf B, Stern D, Yang B, Gatidis S. Uncertainty estimation and explainability in deep learning-based age estimation of the human brain: Results from the German National Cohort MRI study. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. 92: 101967. PMID 34392229 DOI: 10.1016/j.compmedimag.2021.101967 |
0.153 |
|
| 2017 |
Huang B, Zhang K, Zhang J, Sanchez-Romero R, Glymour C, Schölkopf B. Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. Proceedings. Ieee International Conference On Data Mining. 2017: 913-918. PMID 31068766 DOI: 10.1109/ICDM.2017.114 |
0.15 |
|
| 2017 |
Zhang K, Huang B, Zhang J, Glymour C, Schölkopf B. Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. Ijcai : Proceedings of the Conference. 2017: 1347-1353. PMID 28966540 DOI: 10.24963/ijcai.2017/187 |
0.149 |
|
| 2013 |
Wang Z, Mülling K, Deisenroth MP, Ben Amor H, Vogt D, Schölkopf B, Peters J. Probabilistic movement modeling for intention inference in human-robot interaction International Journal of Robotics Research. 32: 841-858. DOI: 10.1177/0278364913478447 |
0.149 |
|
| 2022 |
Kart T, Fischer M, Winzeck S, Glocker B, Bai W, Bülow R, Emmel C, Friedrich L, Kauczor HU, Keil T, Kröncke T, Mayer P, Niendorf T, Peters A, Pischon T, et al. Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies. Scientific Reports. 12: 18733. PMID 36333523 DOI: 10.1038/s41598-022-23632-9 |
0.149 |
|
| 2001 |
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC. Estimating the support of a high-dimensional distribution. Neural Computation. 13: 1443-71. PMID 11440593 DOI: 10.1162/089976601750264965 |
0.149 |
|
| 2005 |
Rätsch G, Sonnenburg S, Schölkopf B. RASE: recognition of alternatively spliced exons in C.elegans. Bioinformatics (Oxford, England). 21: i369-77. PMID 15961480 DOI: 10.1093/bioinformatics/bti1053 |
0.148 |
|
| 2007 |
Wu M, Schölkopf B. A local learning approach for clustering Advances in Neural Information Processing Systems. 1529-1536. |
0.147 |
|
| 2008 |
Hinterberger T, Widman G, Lal TN, Hill J, Tangermann M, Rosenstiel W, Schölkopf B, Elger C, Birbaumer N. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy & Behavior : E&B. 13: 300-6. PMID 18495541 DOI: 10.1016/J.Yebeh.2008.03.014 |
0.147 |
|
| 2016 |
Schölkopf B, Hogg DW, Wang D, Foreman-Mackey D, Janzing D, Simon-Gabriel CJ, Peters J. Modeling confounding by half-sibling regression. Proceedings of the National Academy of Sciences of the United States of America. 113: 7391-8. PMID 27382154 DOI: 10.1073/Pnas.1511656113 |
0.145 |
|
| 2005 |
Kim KI, Franz MO, Schölkopf B. Iterative kernel principal component analysis for image modeling. Ieee Transactions On Pattern Analysis and Machine Intelligence. 27: 1351-66. PMID 16173181 DOI: 10.1109/TPAMI.2005.181 |
0.142 |
|
| 2023 |
Katiyar P, Schwenck J, Frauenfeld L, Divine MR, Agrawal V, Kohlhofer U, Gatidis S, Kontermann R, Königsrainer A, Quintanilla-Martinez L, la Fougère C, Schölkopf B, Pichler BJ, Disselhorst JA. Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET-MRI data. Nature Biomedical Engineering. PMID 37277483 DOI: 10.1038/s41551-023-01047-9 |
0.142 |
|
| 2005 |
Tsuda K, Shin H, Schölkopf B. Fast protein classification with multiple networks. Bioinformatics (Oxford, England). 21: ii59-65. PMID 16204126 DOI: 10.1093/bioinformatics/bti1110 |
0.14 |
|
| 2023 |
Schreiber J, Boix C, Wook Lee J, Li H, Guan Y, Chang CC, Chang JC, Hawkins-Hooker A, Schölkopf B, Schweikert G, Carulla MR, Canakoglu A, Guzzo F, Nanni L, Masseroli M, et al. The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles. Genome Biology. 24: 79. PMID 37072822 DOI: 10.1186/s13059-023-02915-y |
0.139 |
|
| 2003 |
Weston J, Pérez-Cruz F, Bousquet O, Chapelle O, Elisseeff A, Schölkopf B. Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics (Oxford, England). 19: 764-71. PMID 12691989 |
0.139 |
|
| 2019 |
Runge J, Bathiany S, Bollt E, Camps-Valls G, Coumou D, Deyle E, Glymour C, Kretschmer M, Mahecha MD, Muñoz-Marí J, van Nes EH, Peters J, Quax R, Reichstein M, Scheffer M, et al. Inferring causation from time series in Earth system sciences. Nature Communications. 10: 2553. PMID 31201306 DOI: 10.1038/S41467-019-10105-3 |
0.138 |
|
| 2006 |
Borgwardt KM, Gretton A, Rasch MJ, Kriegel HP, Schölkopf B, Smola AJ. Integrating structured biological data by Kernel Maximum Mean Discrepancy. Bioinformatics (Oxford, England). 22: e49-57. PMID 16873512 DOI: 10.1093/Bioinformatics/Btl242 |
0.137 |
|
| 2011 |
Franc V, Zien A, Schölkopf B. Support vector machines as probabilistic models Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 665-672. |
0.134 |
|
| 2019 |
Blöbaum P, Janzing D, Washio T, Shimizu S, Schölkopf B. Analysis of cause-effect inference by comparing regression errors. Peerj. Computer Science. 5: e169. PMID 33816822 DOI: 10.7717/peerj-cs.169 |
0.131 |
|
| 2006 |
Hill NJ, Lal TN, Schröder M, Hinterberger T, Wilhelm B, Nijboer F, Mochty U, Widman G, Elger C, Schölkopf B, Kübler A, Birbaumer N. Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society. 14: 183-6. PMID 16792289 DOI: 10.1109/Tnsre.2006.875548 |
0.131 |
|
| 2004 |
Chalimourda A, Schölkopf B, Smola AJ. Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks : the Official Journal of the International Neural Network Society. 17: 127-41. PMID 14690713 DOI: 10.1016/S0893-6080(03)00209-0 |
0.131 |
|
| 2011 |
Wang Z, Lampert CH, Mülling K, Schölkopf B, Peters J. Learning anticipation policies for robot table tennis Ieee International Conference On Intelligent Robots and Systems. 332-337. DOI: 10.1109/IROS.2011.6048533 |
0.131 |
|
| 2006 |
Franz MO, Schölkopf B. A unifying view of wiener and volterra theory and polynomial kernel regression. Neural Computation. 18: 3097-118. PMID 17052160 DOI: 10.1162/neco.2006.18.12.3097 |
0.126 |
|
| 2016 |
Grimm DG, Roqueiro D, Salome P, Kleeberger S, Greshake B, Zhu W, Liu C, Lippert C, Stegle O, Schölkopf B, Weigel D, Borgwardt K. easyGWAS: A Cloud-based Platform for Comparing the Results of Genome-wide Association Studies. The Plant Cell. PMID 27986896 DOI: 10.1105/Tpc.16.00551 |
0.124 |
|
| 2023 |
Gupta P, Maharaj T, Weiss M, Rahaman N, Alsdurf H, Minoyan N, Harnois-Leblanc S, Merckx J, Williams A, Schmidt V, St-Charles PL, Patel A, Zhang Y, Buckeridge DL, Pal C, et al. Proactive Contact Tracing. Plos Digital Health. 2: e0000199. PMID 36913342 DOI: 10.1371/journal.pdig.0000199 |
0.124 |
|
| 2011 |
Kam-Thong T, Czamara D, Tsuda K, Borgwardt K, Lewis CM, Erhardt-Lehmann A, Hemmer B, Rieckmann P, Daake M, Weber F, Wolf C, Ziegler A, Pütz B, Holsboer F, Schölkopf B, et al. EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. European Journal of Human Genetics : Ejhg. 19: 465-71. PMID 21150885 DOI: 10.1038/Ejhg.2010.196 |
0.12 |
|
| 2015 |
Schölkopf B, Muandet K, Fukumizu K, Harmeling S, Peters J. Computing functions of random variables via reproducing kernel Hilbert space representations Statistics and Computing. 25: 755-766. DOI: 10.1007/s11222-015-9558-5 |
0.118 |
|
| 2014 |
Daneshmand H, Gomez-Rodriguez M, Song L, Schölkopf B. Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm. Proceedings of the ... International Conference On Machine Learning. International Conference On Machine Learning. 2014: 793-801. PMID 25932466 |
0.118 |
|
| 2019 |
Gomez-Gonzalez S, Nemmour Y, Schölkopf B, Peters J. Reliable Real-Time Ball Tracking for Robot Table Tennis Robotics. 8: 90. DOI: 10.3390/Robotics8040090 |
0.115 |
|
| 2004 |
Romdhani S, Torr P, Schölkopf B, Blake A. Efficient face detection by a cascaded support–vector machine expansion Proceedings of the Royal Society of London. Series a: Mathematical, Physical and Engineering Sciences. 460: 3283-3297. DOI: 10.1098/Rspa.2004.1333 |
0.11 |
|
| 2008 |
Steinke F, Hein M, Peters J, Schölkopf B. Manifold-valued thin-plate splines with applications in computer graphics Computer Graphics Forum. 27: 437-448. DOI: 10.1111/J.1467-8659.2008.01141.X |
0.108 |
|
| 2021 |
von Kugelgen J, Gresele L, Scholkopf B. Simpson's Paradox in COVID-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects. Ieee Transactions On Artificial Intelligence. 2: 18-27. PMID 35233556 DOI: 10.1109/TAI.2021.3073088 |
0.107 |
|
| 2011 |
Peters J, Janzing D, Scholkopf B. Causal Inference on Discrete Data using Additive Noise Models. Ieee Transactions On Pattern Analysis and Machine Intelligence. PMID 21464504 DOI: 10.1109/Tpami.2011.71 |
0.106 |
|
| 2023 |
Mehrjou A, Soleymani A, Abyaneh A, Bhatt S, Schölkopf B, Bauer S. Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases. Plos Computational Biology. 19: e1010799. PMID 36689461 DOI: 10.1371/journal.pcbi.1010799 |
0.105 |
|
| 2013 |
Muandet K, Schölkopf B. One-class support measure machines for group anomaly detection Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, Uai 2013. 449-458. |
0.104 |
|
| 2000 |
Scholkopf B, Smola AJ, Williamson RC, Bartlett PL. New support vector algorithms Neural Computation. 12: 1207-45. PMID 10905814 |
0.103 |
|
| 2011 |
Hirsch M, Schölkopf B, Habeck M. A blind deconvolution approach for improving the resolution of cryo-EM density maps. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology. 18: 335-46. PMID 21385038 DOI: 10.1089/cmb.2010.0264 |
0.103 |
|
| 2014 |
Bensch M, Martens S, Halder S, Hill J, Nijboer F, Ramos A, Birbaumer N, Bogdan M, Kotchoubey B, Rosenstiel W, Schölkopf B, Gharabaghi A. Assessing attention and cognitive function in completely locked-in state with event-related brain potentials and epidural electrocorticography. Journal of Neural Engineering. 11: 026006. PMID 24556584 DOI: 10.1088/1741-2560/11/2/026006 |
0.102 |
|
| 2009 |
Martens S, Farquhar J, Hill J, Schölkopf B. Graphical models for decoding in BCI visual speller systems 2009 4th International Ieee/Embs Conference On Neural Engineering, Ner '09. 470-473. DOI: 10.1109/NER.2009.5109335 |
0.101 |
|
| 2016 |
Gong M, Zhang K, Liu T, Tao D, Glymour C, Schölkopf B. Domain Adaptation with Conditional Transferable Components. Jmlr Workshop and Conference Proceedings. 48: 2839-2848. PMID 28239433 |
0.101 |
|
| 2010 |
Zhang K, Schölkopf B, Janzing D. Invariant Gaussian process latent variable models and application in causal discovery Proceedings of the 26th Conference On Uncertainty in Artificial Intelligence, Uai 2010. 717-724. |
0.101 |
|
| 2011 |
Hirsch M, Harmeling S, Sra S, Schölkopf B. Online multi-frame blind deconvolution with super-resolution and saturation correction Astronomy and Astrophysics. 531. DOI: 10.1051/0004-6361/200913955 |
0.099 |
|
| 2012 |
Köhler R, Hirsch M, Mohler B, Schölkopf B, Harmeling S. Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7578: 27-40. DOI: 10.1007/978-3-642-33786-4_3 |
0.099 |
|
| 2014 |
Köhler R, Schuler C, Schölkopf B, Harmeling S. Mask-specific inpainting with deep neural networks Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8753: 523-534. DOI: 10.1007/978-3-319-11752-2_43 |
0.098 |
|
| 2010 |
Steinke F, Hein M, Schölkopf B. Nonparametric regression between general riemannian manifolds Siam Journal On Imaging Sciences. 3: 527-563. DOI: 10.1137/080744189 |
0.098 |
|
| 2011 |
Hofmann M, Bezrukov I, Mantlik F, Aschoff P, Steinke F, Beyer T, Pichler BJ, Schölkopf B. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. 52: 1392-9. PMID 21828115 DOI: 10.2967/jnumed.110.078949 |
0.097 |
|
| 2010 |
Peters J, Janzing D, Gretton A, Schölkopf B. Kernel methods for detecting the direction of time series Studies in Classification, Data Analysis, and Knowledge Organization. 57-66. DOI: 10.1007/978-3-642-01044-6-5 |
0.096 |
|
| 2021 |
Dietz B, Machann J, Agrawal V, Heni M, Schwab P, Dienes J, Reichert S, Birkenfeld AL, Häring HU, Schick F, Stefan N, Fritsche A, Preissl H, Schölkopf B, Bauer S, et al. Detection of diabetes from whole-body MRI using deep learning. Jci Insight. 6. PMID 34591793 DOI: 10.1172/jci.insight.146999 |
0.096 |
|
| 2013 |
Muandet K, Balduzzi D, Schölkopf B. Domain generalization via invariant feature representation 30th International Conference On Machine Learning, Icml 2013. 10-18. |
0.096 |
|
| 2013 |
Loktyushin A, Nickisch H, Pohmann R, Schölkopf B. Blind retrospective motion correction of MR images. Magnetic Resonance in Medicine. 70: 1608-18. PMID 23401078 DOI: 10.1002/mrm.24615 |
0.095 |
|
| 2007 |
Waldert S, Bensch M, Bogdan M, Rosenstiel W, Schölkopf B, Lowery CL, Eswaran H, Preissl H. Real-time fetal heart monitoring in biomagnetic measurements using adaptive real-time ICA. Ieee Transactions On Bio-Medical Engineering. 54: 1867-74. PMID 17926685 DOI: 10.1109/Tbme.2007.895749 |
0.094 |
|
| 2018 |
Loktyushin A, Ehses P, Schölkopf B, Scheffler K. Autofocusing-based phase correction. Magnetic Resonance in Medicine. PMID 29352498 DOI: 10.1002/mrm.27092 |
0.093 |
|
| 2012 |
Kam-Thong T, Azencott CA, Cayton L, Pütz B, Altmann A, Karbalai N, Sämann PG, Schölkopf B, Müller-Myhsok B, Borgwardt KM. GLIDE: GPU-based linear regression for detection of epistasis. Human Heredity. 73: 220-36. PMID 22965145 DOI: 10.1159/000341885 |
0.093 |
|
| 2015 |
Zhang K, Wang Z, Zhang J, Schölkopf B. On estimation of functional causal models: General results and application to the post-nonlinear causal model Acm Transactions On Intelligent Systems and Technology. 7. DOI: 10.1145/2700476 |
0.09 |
|
| 2019 |
Aghaeifar A, Zhou J, Heule R, Tabibian B, Schölkopf B, Jia F, Zaitsev M, Scheffler K. A 32-channel multi-coil setup optimized for human brain shimming at 9.4T. Magnetic Resonance in Medicine. PMID 31483527 DOI: 10.1002/mrm.27929 |
0.09 |
|
| 2013 |
Schultz T, Schlaffke L, Schölkopf B, Schmidt-Wilcke T. HiFiVE: A hilbert space embedding of fiber variability estimates for uncertainty modeling and visualization Computer Graphics Forum. 32: 121-130. DOI: 10.1111/cgf.12099 |
0.088 |
|
| 2022 |
Gatidis S, Kart T, Fischer M, Winzeck S, Glocker B, Bai W, Bülow R, Emmel C, Friedrich L, Kauczor HU, Keil T, Kröncke T, Mayer P, Niendorf T, Peters A, et al. Better Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort. Investigative Radiology. PMID 36729536 DOI: 10.1097/RLI.0000000000000941 |
0.087 |
|
| 2023 |
Athanassiadis AG, Schlieder L, Melde K, Volchkov V, Scholkopf B, Fischer P. Multiplane Diffractive Acoustic Networks. Ieee Transactions On Ultrasonics, Ferroelectrics, and Frequency Control. 70: 441-448. PMID 37028299 DOI: 10.1109/TUFFC.2023.3255992 |
0.087 |
|
| 2012 |
Janzing D, Mooij J, Zhang K, Lemeire J, Zscheischler J, Daniušis P, Steudel B, Schölkopf B. Information-geometric approach to inferring causal directions Artificial Intelligence. 182: 1-31. DOI: 10.1016/j.artint.2012.01.002 |
0.087 |
|
| 2010 |
Daniufisis P, Janzing D, Mooij J, Zscheischler J, Steudel B, Zhang K, Schölkopf B. Inferring deterministic causal relations Proceedings of the 26th Conference On Uncertainty in Artificial Intelligence, Uai 2010. 143-150. |
0.087 |
|
| 2015 |
Foreman-Mackey D, Montet BT, Hogg DW, Morton TD, Wang D, Schölkopf B. A SYSTEMATIC SEARCH FOR TRANSITING PLANETS IN THE K2 DATA Astrophysical Journal. 806. DOI: 10.1088/0004-637X/806/2/215 |
0.085 |
|
| 2019 |
Scheffler K, Loktyushin A, Bause J, Aghaeifar A, Steffen T, Schölkopf B. Spread-spectrum magnetic resonance imaging. Magnetic Resonance in Medicine. PMID 31025413 DOI: 10.1002/mrm.27766 |
0.085 |
|
| 2010 |
Schwamberger V, Le PHD, Schölkopf B, Franz MO. The influence of the image basis on modeling and steganalysis performance Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6387: 133-144. DOI: 10.1007/978-3-642-16435-4_11 |
0.084 |
|
| 2015 |
Sgouritsa E, Janzing D, Hennig P, Schölkopf B. Inference of cause and effect with unsupervised inverse regression Journal of Machine Learning Research. 38: 847-855. |
0.082 |
|
| 2012 |
Dinuzzo F, Schölkopf B. The representer theorem for Hilbert spaces: A necessary and sufficient condition Advances in Neural Information Processing Systems. 1: 189-196. |
0.081 |
|
| 2008 |
Walder C, Kwang IK, Schölkopf B. Sparse multiscale Gaussian process regression Proceedings of the 25th International Conference On Machine Learning. 1112-1119. |
0.08 |
|
| 2023 |
Melde K, Kremer H, Shi M, Seneca S, Frey C, Platzman I, Degel C, Schmitt D, Schölkopf B, Fischer P. Compact holographic sound fields enable rapid one-step assembly of matter in 3D. Science Advances. 9: eadf6182. PMID 36753553 DOI: 10.1126/sciadv.adf6182 |
0.08 |
|
| 2010 |
Janzing D, Hoyer PO, Schölkopf B. Telling cause from effect based on high-dimensional observations Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 479-486. |
0.079 |
|
| 2011 |
Zhang K, Peters J, Janzing D, Schölkopf B. Kernel-based conditional independence test and application in causal discovery Proceedings of the 27th Conference On Uncertainty in Artificial Intelligence, Uai 2011. 804-813. |
0.079 |
|
| 2010 |
Mooij JM, Stegle O, Janzing D, Zhang K, Schölkopf B. Probabilistic latent variable models for distinguishing between cause and effect Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.078 |
|
| 2008 |
Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, Brady M, Schölkopf B, Pichler BJ. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. 49: 1875-83. PMID 18927326 DOI: 10.2967/jnumed.107.049353 |
0.078 |
|
| 2008 |
Sun X, Janzing D, Schölkopf B. Causal reasoning by evaluating the complexity of conditional densities with kernel methods Neurocomputing. 71: 1248-1256. DOI: 10.1016/j.neucom.2007.12.023 |
0.077 |
|
| 2010 |
Camps-Valls G, Mooij J, Schölkopf B. Remote sensing feature selection by kernel dependence measures Ieee Geoscience and Remote Sensing Letters. 7: 587-591. DOI: 10.1109/LGRS.2010.2041896 |
0.077 |
|
| 2014 |
Chen Z, Zhang K, Chan L, Schölkopf B. Causal discovery via reproducing kernel Hilbert space embeddings. Neural Computation. 26: 1484-517. PMID 24708374 DOI: 10.1162/Neco_A_00599 |
0.076 |
|
| 2009 |
Sriperumbudur BK, Fukumizu K, Gretton A, Lanckriet GRG, Schölkopf B. Kernel choice and classifiability for RKHS embeddings of probability distributions Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1750-1758. |
0.075 |
|
| 2015 |
Janzing D, Steudel B, Shajarisales N, Schölkopf B. Justifying information-geometric causal inference Measures of Complexity: Festschrift For Alexey Chervonenkis. 253-265. DOI: 10.1007/978-3-319-21852-6_18 |
0.075 |
|
| 2010 |
Harmeling S, Hirsch M, Schölkopf B. Space-variant single-image blind deconvolution for removing camera shake Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.073 |
|
| 2013 |
Seldin Y, Schölkopf B. On the relations and differences between popper dimension, exclusion dimension and VC-dimension Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 53-57. DOI: 10.1007/978-3-642-41136-6_6 |
0.073 |
|
| 2010 |
Janzing D, Schölkopf B. Causal inference using the algorithmic Markov condition Ieee Transactions On Information Theory. 56: 5168-5194. DOI: 10.1109/TIT.2010.2060095 |
0.072 |
|
| 2013 |
Gomez-Rodriguez M, Leskovec J, Schölkopf B. Modeling information propagation with survival theory 30th International Conference On Machine Learning, Icml 2013. 1703-1711. |
0.072 |
|
| 2005 |
Chalimourda A, Schölkopf B, Smola AJ. Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks : the Official Journal of the International Neural Network Society. 18: 205. PMID 15795117 DOI: 10.1016/j.neunet.2004.11.001 |
0.071 |
|
| 1998 |
Schölkopf B. The moon tilt illusion. Perception. 27: 1229-32. PMID 10505201 |
0.07 |
|
| 2011 |
Gehler PV, Rother C, Kiefel M, Zhang L, Schölkopf B. Recovering intrinsic images with a global sparsity prior on reflectance Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.069 |
|
| 2010 |
Sriperumbudur BK, Gretton A, Fukumizu K, Schölkopf B, Lanckriet GRG. Hilbert space embeddings and metrics on probability measures Journal of Machine Learning Research. 11: 1517-1561. |
0.069 |
|
| 2009 |
Schweikert G, Widmer C, Schölkopf B, Rätsch G. An empirical analysis of domain adaptation algorithms for genomic sequence analysis Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1433-1440. |
0.069 |
|
| 2014 |
Zscheischler J, Mahecha MD, Von Buttlar J, Harmeling S, Jung M, Rammig A, Randerson JT, Schölkopf B, Seneviratne SI, Tomelleri E, Zaehle S, Reichstein M. A few extreme events dominate global interannual variability in gross primary production Environmental Research Letters. 9. DOI: 10.1088/1748-9326/9/3/035001 |
0.068 |
|
| 2010 |
Peters J, Janzing D, Schölkopf B. Identifying cause and effect on discrete data using additive noise models Journal of Machine Learning Research. 9: 597-604. |
0.068 |
|
| 2008 |
Charpiat G, Hofmann M, Schölkopf B. Automatic image colorization via multimodal predictions Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5304: 126-139. DOI: 10.1007/978-3-540-88690-7-10 |
0.068 |
|
| 2008 |
Freeman W, Perona P, Schölkopf B. International Journal of Computer Vision: Guest Editorial International Journal of Computer Vision. 77: 1. DOI: 10.1007/s11263-008-0127-7 |
0.067 |
|
| 2009 |
Mooij J, Janzing D, Peters J, Schölkopf B. Regression by dependence minimization and its application to causal inference in additive noise models Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 745-752. DOI: 10.1145/1553374.1553470 |
0.067 |
|
| 2013 |
Soekadar SR, Born J, Birbaumer N, Bensch M, Halder S, Murguialday AR, Gharabaghi A, Nijboer F, Schölkopf B, Martens S. Fragmentation of slow wave sleep after onset of complete locked-in state. Journal of Clinical Sleep Medicine : Jcsm : Official Publication of the American Academy of Sleep Medicine. 9: 951-3. PMID 23997708 DOI: 10.5664/Jcsm.3002 |
0.067 |
|
| 2015 |
Loktyushin A, Nickisch H, Pohmann R, Schölkopf B. Blind multirigid retrospective motion correction of MR images. Magnetic Resonance in Medicine. 73: 1457-68. PMID 24760736 DOI: 10.1002/mrm.25266 |
0.067 |
|
| 2009 |
Sra S, Kim D, Dhillon I, Schölkopf B. A new non-monotonic algorithm for PET image reconstruction Ieee Nuclear Science Symposium Conference Record. 2500-2502. DOI: 10.1109/NSSMIC.2009.5402060 |
0.066 |
|
| 2007 |
Walder C, Schölkopf B, Chapelle O. Implicit surfaces with globally regularised and compactly supported basis functions Advances in Neural Information Processing Systems. 273-280. |
0.066 |
|
| 2009 |
Peters J, Janzing D, Gretton A, Schölkopf B. Detecting the direction of causal time series Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 801-808. DOI: 10.1145/1553374.1553477 |
0.066 |
|
| 2009 |
Hill J, Farquhar J, Martens S, Bießmann F, Schölkopf B. Effects of stimulus type and of error-correcting code design on BCI speller performance Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 665-672. |
0.066 |
|
| 2009 |
Gretton A, Fukumizu K, Teo CH, Song L, Schölkopf B, Smola AJ. A kernel statistical test of independence Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.066 |
|
| 2009 |
Harmeling S, Hirsch M, Sra S, Schölkopf B. Online blind deconvolution for astronomical imaging 2009 Ieee International Conference On Computational Photography, Iccp 09. DOI: 10.1109/ICCPHOT.2009.5559014 |
0.064 |
|
| 2014 |
Gomez-Rodriguez M, Song L, Schölkopf B. Open problem: Finding good cascade sampling processes for the network inference problem Journal of Machine Learning Research. 35: 1276-1279. |
0.064 |
|
| 2008 |
Sriperumbudur BK, Gretton A, Fukumizu K, Lanckriet G, Schölkopf B. Injective hilbert space embeddings of probability measures 21st Annual Conference On Learning Theory, Colt 2008. 111-122. |
0.064 |
|
| 2010 |
Hirsch M, Schölkopf B, Habeck M. A new algorithm for improving the resolution of cryo-EM density maps Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6044: 174-188. DOI: 10.1007/978-3-642-12683-3_12 |
0.064 |
|
| 2022 |
Gatidis S, Hepp T, Früh M, La Fougère C, Nikolaou K, Pfannenberg C, Schölkopf B, Küstner T, Cyran C, Rubin D. A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions. Scientific Data. 9: 601. PMID 36195599 DOI: 10.1038/s41597-022-01718-3 |
0.063 |
|
| 2009 |
Hofmann M, Pichler B, Schölkopf B, Beyer T. Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques. European Journal of Nuclear Medicine and Molecular Imaging. 36: S93-104. PMID 19104810 DOI: 10.1007/s00259-008-1007-7 |
0.062 |
|
| 2007 |
Sun X, Janzing D, Schölkopf B. Distinguishing between cause and effect via kernel-based complexity measures for conditional distributions Esann 2007 Proceedings - 15th European Symposium On Artificial Neural Networks. 441-446. |
0.062 |
|
| 2010 |
Álvarez MA, Peters J, Schölkopf B, Lawrence ND. Switched latent force models for movement segmentation Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.062 |
|
| 2015 |
Kopp M, Harmeling S, Schütz G, Schölkopf B, Fähnle M. Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter. Ultramicroscopy. 148: 115-22. PMID 25461588 DOI: 10.1016/J.Ultramic.2014.10.001 |
0.062 |
|
| 2021 |
Schwab P, Mehrjou A, Parbhoo S, Celi LA, Hetzel J, Hofer M, Schölkopf B, Bauer S. Real-time prediction of COVID-19 related mortality using electronic health records. Nature Communications. 12: 1058. PMID 33594046 DOI: 10.1038/s41467-020-20816-7 |
0.061 |
|
| 2014 |
Doran G, Muandet K, Zhang K, Schölkopf B. A permutation-based kernel conditional independence test Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, Uai 2014. 132-141. |
0.06 |
|
| 2010 |
Steudel B, Janzing D, Schölkopf B. Causal Markov condition for submodular information measures Colt 2010 - the 23rd Conference On Learning Theory. 464-476. |
0.059 |
|
| 2012 |
Gomez-Rodriguez M, Schölkopf B. Submodular inference of diffusion networks from multiple trees Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 489-496. |
0.058 |
|
| 2022 |
Barthe G, Viti R, Druschel P, Garg D, Gomez-Rodriguez M, Ingo P, Kremer H, Lentz M, Lorch L, Mehta A, Schölkopf B. Listening to bluetooth beacons for epidemic risk mitigation. Scientific Reports. 12: 5558. PMID 35365709 DOI: 10.1038/s41598-022-09440-1 |
0.058 |
|
| 2007 |
Gretton A, Borgwardt KM, Rasch M, Schölkopf B, Smola AJ. A kernel method for the two-sample-problem Advances in Neural Information Processing Systems. 513-520. |
0.058 |
|
| 2009 |
Fukumizu K, Gretton A, Sun X, Schölkopf B. Kernel measures of conditional dependence Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.058 |
|
| 2008 |
Song L, Zhang X, Smola A, Gretton A, Schölkopf B. Tailoring density estimation via reproducing kernel moment matching Proceedings of the 25th International Conference On Machine Learning. 992-999. |
0.058 |
|
| 2015 |
Khatami M, Schmidt-Wilcke T, Sundgren PC, Abbasloo A, Schölkopf B, Schultz T. BundleMAP: Anatomically localized features from dMRI for detection of disease Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9352: 52-60. DOI: 10.1007/978-3-319-24888-2_7 |
0.058 |
|
| 2009 |
Shin H, Tsuda K, Schölkopf B. Protein functional class prediction with a combined graph Expert Systems With Applications. 36: 3284-3292. DOI: 10.1016/j.eswa.2008.01.006 |
0.057 |
|
| 2009 |
Georgii E, Tsuda K, Schölkopf B. Multi-way set enumeration in real-valued tensors Proceedings of the 2009 Workshop On Data Mining Using Matrices and Tensors, Dmmt'09 in Conjunction With the 15th Acm Sigkdd International Conference On Knowledge Discovery and Data Mining, Sigkdd 2009. |
0.057 |
|
| 2020 |
Mineeva O, Rojas-Carulla M, Ley RE, Schölkopf B, Youngblut ND. DeepMAsED: Evaluating the quality of metagenomic assemblies. Bioinformatics (Oxford, England). PMID 32096824 DOI: 10.1093/Bioinformatics/Btaa124 |
0.057 |
|
| 2014 |
Kpotufe S, Sgouritsa E, Janzing D, Schölkopf B. Consistency of causal inference under the additive noise model 31st International Conference On Machine Learning, Icml 2014. 2: 1849-1857. |
0.057 |
|
| 2016 |
Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Schölkopf B, Pichler BJ, Disselhorst JA. A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation. Molecular Imaging and Biology : Mib : the Official Publication of the Academy of Molecular Imaging. PMID 27734253 DOI: 10.1007/s11307-016-1009-y |
0.057 |
|
| 2013 |
Bezrukov I, Schmidt H, Mantlik F, Schwenzer N, Brendle C, Schölkopf B, Pichler BJ. MR-based attenuation correction methods for improved PET quantification in lesions within bone and susceptibility artifact regions. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. 54: 1768-74. PMID 24009273 DOI: 10.2967/jnumed.112.113209 |
0.056 |
|
| 2014 |
Lopez-Paz D, Sra S, Smola AJ, Ghahramani Z, Schölkopf B. Randomized nonlinear component analysis 31st International Conference On Machine Learning, Icml 2014. 4: 3196-3204. |
0.056 |
|
| 2007 |
Smola A, Gretton A, Song L, Schölkopf B. A hilbert space embedding for distributions Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4755: 40-41. |
0.055 |
|
| 2012 |
Gomez-Rodriguez M, Schölkopf B. Influence maximization in continuous time diffusion networks Proceedings of the 29th International Conference On Machine Learning, Icml 2012. 1: 313-320. |
0.055 |
|
| 2013 |
Peters J, Janzing D, Schölkopf B. Causal inference on time series using restricted Structural Equation Models Advances in Neural Information Processing Systems. |
0.055 |
|
| 2015 |
Loktyushin A, Schuler C, Scheffler K, Schölkopf B. Retrospective motion correction of magnitude-input MR images Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9487: 3-12. DOI: 10.1007/978-3-319-27929-9_1 |
0.055 |
|
| 2016 |
Katiyar P, Divine MR, Kohlhofer U, Quintanilla-Martinez L, Schölkopf B, Pichler BJ, Disselhorst JA. Spectral Clustering predicts tumor tissue heterogeneity using dynamic 18F-FDG PET: a complement to the standard compartmental modeling approach. Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine. PMID 27811120 DOI: 10.2967/jnumed.116.181370 |
0.055 |
|
| 2014 |
Muandet K, Fukumizu K, Sriperumbudur B, Gretton A, Schölkopf B. Kernel mean estimation and stein effect 31st International Conference On Machine Learning, Icml 2014. 1: 12-36. |
0.053 |
|
| 2012 |
Lopez-Paz D, Hernández-Lobato JM, Schölkopf B. Semi-supervised domain adaptation with non-parametric copulas Advances in Neural Information Processing Systems. 1: 665-673. |
0.053 |
|
| 2011 |
Mantlik F, Hofmann M, Werner MK, Sauter A, Kupferschläger J, Schölkopf B, Pichler BJ, Beyer T. The effect of patient positioning aids on PET quantification in PET/MR imaging. European Journal of Nuclear Medicine and Molecular Imaging. 38: 920-9. PMID 21308373 DOI: 10.1007/s00259-010-1721-9 |
0.053 |
|
| 2014 |
Peters J, Mooij JM, Janzing D, Schölkopf B. Causal discovery with continuous additive noise models Journal of Machine Learning Research. 15: 2009-2053. |
0.052 |
|
| 2023 |
Kekić A, Dehning J, Gresele L, von Kügelgen J, Priesemann V, Schölkopf B. Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Patterns (New York, N.Y.). 4: 100739. PMID 37304758 DOI: 10.1016/j.patter.2023.100739 |
0.052 |
|
| 2007 |
Clark RM, Schweikert G, Toomajian C, Ossowski S, Zeller G, Shinn P, Warthmann N, Hu TT, Fu G, Hinds DA, Chen H, Frazer KA, Huson DH, Schölkopf B, Nordborg M, et al. Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science (New York, N.Y.). 317: 338-42. PMID 17641193 DOI: 10.1126/Science.1138632 |
0.051 |
|
| 2009 |
Walder C, Schölkopf B. Diffeomorphic dimensionality reduction Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1713-1720. |
0.051 |
|
| 2011 |
Gomez-Rodriguez M, Balduzzi D, Schölkopf B. Uncovering the temporal dynamics of diffusion networks Proceedings of the 28th International Conference On Machine Learning, Icml 2011. 561-568. |
0.051 |
|
| 2014 |
Chaves R, Luft L, Maciel TO, Gross D, Janzing D, Schölkopf B. Inferring latent structures via information inequalities Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, Uai 2014. 112-121. |
0.05 |
|
| 2013 |
Sgouritsa E, Janzing D, Peters J, Schölkopf B. Identifying finite mixtures of nonparametric product distributions and causal inference of confounders Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, Uai 2013. 556-565. |
0.049 |
|
| 2009 |
Jegelka S, Gretton A, Schölkopf B, Sriperumbudur BK, Von Luxburg U. Generalized clustering via kernel embeddings Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5803: 144-152. DOI: 10.1007/978-3-642-04617-9_19 |
0.049 |
|
| 2008 |
Laubinger S, Zeller G, Henz SR, Sachsenberg T, Widmer CK, Naouar N, Vuylsteke M, Schölkopf B, Rätsch G, Weigel D. At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana. Genome Biology. 9: R112. PMID 18613972 DOI: 10.1186/Gb-2008-9-7-R112 |
0.047 |
|
| 2007 |
Huang J, Smola AJ, Gretton A, Borgwardt KM, Schölkopf B. Correcting sample selection bias by unlabeled data Advances in Neural Information Processing Systems. 601-608. |
0.046 |
|
| 2012 |
Sriperumbudur BK, Fukumizu K, Gretton A, Schölkopf B, Lanckriet GRG. On the empirical estimation of integral probability metrics Electronic Journal of Statistics. 6: 1550-1599. DOI: 10.1214/12-EJS722 |
0.046 |
|
| 2011 |
Mooij JM, Janzing D, Heskes T, Schölkopf B. On causal discovery with cyclic additive noise models Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.046 |
|
| 2013 |
Gomez Rodriguez M, Leskovec J, Schölkopf B. Structure and dynamics of information pathways in online media Wsdm 2013 - Proceedings of the 6th Acm International Conference On Web Search and Data Mining. 23-32. DOI: 10.1145/2433396.2433402 |
0.045 |
|
| 2010 |
Sriperumbudur BK, Fukumizu K, Gretton A, Schölkopf B, Lanckriet GRG. Non-parametric estimation of integral probability metrics Ieee International Symposium On Information Theory - Proceedings. 1428-1432. DOI: 10.1109/ISIT.2010.5513626 |
0.045 |
|
| 2005 |
Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Schölkopf B, Weigel D, Lohmann JU. A gene expression map of Arabidopsis thaliana development. Nature Genetics. 37: 501-6. PMID 15806101 DOI: 10.1038/Ng1543 |
0.045 |
|
| 2014 |
Geiger P, Janzing D, Schölkopf B. Estimating causal effects by bounding confounding Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, Uai 2014. 240-249. |
0.044 |
|
| 2009 |
Fukumizu K, Sriperumbudur B, Gretton A, Schölkopf B. Characteristic kernels on groups and semigroups Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 473-480. |
0.043 |
|
| 2013 |
Bezrukov I, Mantlik F, Schmidt H, Schölkopf B, Pichler BJ. MR-Based PET attenuation correction for PET/MR imaging. Seminars in Nuclear Medicine. 43: 45-59. PMID 23178088 DOI: 10.1053/j.semnuclmed.2012.08.002 |
0.043 |
|
| 2009 |
Seeger MW, Nickisch H, Pohmann R, Schölkopf B. Bayesian experimental design of magnetic resonance imaging sequences Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1441-1448. |
0.042 |
|
| 2009 |
Hoyer PO, Janzing D, Mooij J, Peters J, Schölkopf B. Nonlinear causal discovery with additive noise models Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 689-696. |
0.042 |
|
| 2009 |
Janzing D, Peters J, Mooij J, Schölkopf B. Identifying confounders using additive noise models Proceedings of the 25th Conference On Uncertainty in Artificial Intelligence, Uai 2009. 249-257. |
0.042 |
|
| 2013 |
Lopez-Paz D, Hennig P, Schölkopf B. The randomized dependence coefficient Advances in Neural Information Processing Systems. |
0.041 |
|
| 2013 |
Schölkopf B, Luo Z, Vovk V. Empirical inference: Festschrift in honor of Vladimir N. Vapnik Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 1-287. DOI: 10.1007/978-3-642-41136-6 |
0.04 |
|
| 2011 |
Burger HC, Schölkopf B, Harmeling S. Removing noise from astronomical images using a pixel-specific noise model 2011 Ieee International Conference On Computational Photography, Iccp 2011. DOI: 10.1109/ICCPHOT.2011.5753128 |
0.038 |
|
| 2011 |
Georgii E, Tsuda K, Schölkopf B. Multi-way set enumeration in weight tensors Machine Learning. 82: 123-155. DOI: 10.1007/s10994-010-5210-y |
0.038 |
|
| 2013 |
Köhler R, Hirsch M, Schölkopf B, Harmeling S. Improving alpha matting and motion blurred foreground estimation 2013 Ieee International Conference On Image Processing, Icip 2013 - Proceedings. 3446-3450. DOI: 10.1109/ICIP.2013.6738711 |
0.038 |
|
| 2011 |
Achlioptas P, Schölkopf B, Borgwardt KM. Two-locus association mapping in subquadratic time Proceedings of the Acm Sigkdd International Conference On Knowledge Discovery and Data Mining. 726-734. DOI: 10.1145/2020408.2020521 |
0.032 |
|
| 2013 |
Mooij JM, Janzing D, Schölkopf B. From ordinary differential equations to structural causal models: The deterministic case Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, Uai 2013. 440-448. |
0.031 |
|
| 2014 |
Muandet K, Sriperumbudur B, Schölkopf B. Kernel mean estimation via spectral filtering Advances in Neural Information Processing Systems. 1: 1-9. |
0.03 |
|
| 2010 |
Hirsch M, Sra S, Schölkopf B, Harmeling S. Efficient filter flow for space-variant multiframe blind deconvolution Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 607-614. DOI: 10.1109/CVPR.2010.5540158 |
0.029 |
|
| 2011 |
Hirsch M, Schuler CJ, Harmeling S, Schölkopf B. Fast removal of non-uniform camera shake Proceedings of the Ieee International Conference On Computer Vision. 463-470. DOI: 10.1109/ICCV.2011.6126276 |
0.028 |
|
| 2009 |
Walder C, Breidt M, Bülthoff H, Schölkopf B, Curio C. Markerless 3d face tracking Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5748: 41-50. DOI: 10.1007/978-3-642-03798-6_5 |
0.026 |
|
| 2012 |
Gretton A, Borgwardt KM, Rasch MJ, Schölkopf B, Smola A. A kernel two-sample test Journal of Machine Learning Research. 13: 723-773. |
0.026 |
|
| 2011 |
Janzing D, Sgouritsa E, Stegle O, Peters J, Schölkopf B. Detecting low-complexity unobserved causes Proceedings of the 27th Conference On Uncertainty in Artificial Intelligence, Uai 2011. 383-391. |
0.023 |
|
| 2008 |
Steinke F, Schölkopf B. Kernels, regularization and differential equations Pattern Recognition. 41: 3271-3286. DOI: 10.1016/j.patcog.2008.06.011 |
0.022 |
|
| 2014 |
Lang D, Hogg DW, Schölkopf B. Towards building a Crowd-Sourced Sky Map Journal of Machine Learning Research. 33: 549-557. |
0.021 |
|
| 2014 |
Gomez-Rodriguez M, Gummadi KP, Schölkopf B. Quantifying information overload in social media and its impact on social contagions Proceedings of the 8th International Conference On Weblogs and Social Media, Icwsm 2014. 170-179. |
0.017 |
|
| 2012 |
Schuler CJ, Hirsch M, Harmeling S, Schölkopf B. Blind correction of optical aberrations Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7574: 187-200. DOI: 10.1007/978-3-642-33712-3_14 |
0.016 |
|
| 2009 |
Gomez-Rodriguez M, Kober J, Schölkopf B. Denoising photographs using dark frames optimized by quadratic programming 2009 Ieee International Conference On Computational Photography, Iccp 09. DOI: 10.1109/ICCPHOT.2009.5559013 |
0.015 |
|
| 2018 |
Babbar R, Heni M, Peter A, Hrabě de Angelis M, Häring HU, Fritsche A, Preissl H, Schölkopf B, Wagner R. Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test. Frontiers in Endocrinology. 9: 82. PMID 29615972 DOI: 10.3389/fendo.2018.00082 |
0.015 |
|
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