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2020 – today
- 2024
[j17]Lisa M. Koch
, Christian F. Baumgartner, Philipp Berens:
Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. npj Digit. Medicine 7(1) (2024)- [j16]Rita González-Márquez, Luca Schmidt, Benjamin M. Schmidt, Philipp Berens, Dmitry Kobak:
The landscape of biomedical research. Patterns 5(6): 100968 (2024) - [j15]Rita González-Márquez, Philipp Berens, Dmitry Kobak:
Meet the authors: Rita González-Márquez, Philipp Berens, and Dmitry Kobak. Patterns 5(6): 100993 (2024) - [c21]Max F. Burg, Thomas Zenkel, Michaela Vystrcilová, Jonathan Oesterle, Larissa Höfling, Konstantin F. Willeke, Jan Lause, Sarah Müller, Paul G. Fahey, Zhiwei Ding, Kelli Restivo, Shashwat Sridhar, Tim Gollisch, Philipp Berens, Andreas S. Tolias, Thomas Euler, Matthias Bethge, Alexander S. Ecker:
Most discriminative stimuli for functional cell type clustering. ICLR 2024 - [c20]Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens:
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. ICML 2024 - [c19]Julius Gervelmeyer, Sarah Müller, Kerol Djoumessi, David Merle, Simon J. Clark, Lisa M. Koch, Philipp Berens:
Interpretable-by-Design Deep Survival Analysis for Disease Progression Modeling. MICCAI (10) 2024: 502-512 - [c18]Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein, Philipp Berens, Lisa M. Koch:
This Actually Looks Like that: Proto-BagNets for Local and Global Interpretability-by-Design. MICCAI (10) 2024: 718-728 - [c17]Sarah Müller, Louisa Fay, Lisa M. Koch, Sergios Gatidis, Thomas Küstner, Philipp Berens:
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging. MLMI@MICCAI (2) 2024: 53-62 - [i22]Max F. Burg, Thomas Zenkel, Michaela Vystrcilová, Jonathan Oesterle, Larissa Höfling, Konstantin F. Willeke, Jan Lause, Sarah Müller, Paul G. Fahey, Zhiwei Ding, Kelli Restivo, Shashwat Sridhar, Tim Gollisch, Philipp Berens, Andreas S. Tolias, Thomas Euler, Matthias Bethge, Alexander S. Ecker:
Most discriminative stimuli for functional cell type identification. CoRR abs/2401.05342 (2024) - [i21]Sacha Sokoloski, Jure Majnik, Philipp Berens:
A computational approach to visual ecology with deep reinforcement learning. CoRR abs/2402.05266 (2024) - [i20]Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens:
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. CoRR abs/2402.12231 (2024) - [i19]Ifeoma Veronica Nwabufo, Jan Niklas Böhm, Philipp Berens, Dmitry Kobak:
Self-supervised Visualisation of Medical Image Datasets. CoRR abs/2402.14566 (2024) - [i18]Sarah Müller, Lisa M. Koch, Hendrik P. A. Lensch, Philipp Berens:
Disentangling representations of retinal images with generative models. CoRR abs/2402.19186 (2024) - [i17]Jonathan Fuhr, Philipp Berens, Dominik Papies:
Estimating Causal Effects with Double Machine Learning - A Method Evaluation. CoRR abs/2403.14385 (2024) - [i16]Jeremiah Fadugba, Patrick Köhler, Lisa M. Koch, Petru Manescu, Philipp Berens:
Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization. CoRR abs/2406.14994 (2024) - [i15]Kerol Djoumessi, Bubacarr Bah, Laura Kühlewein, Philipp Berens, Lisa M. Koch:
This actually looks like that: Proto-BagNets for local and global interpretability-by-design. CoRR abs/2406.15168 (2024) - [i14]Sarah Müller, Louisa Fay, Lisa M. Koch, Sergios Gatidis, Thomas Küstner, Philipp Berens:
Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging. CoRR abs/2407.18792 (2024) - 2023
- [c16]Jan Niklas Böhm, Philipp Berens, Dmitry Kobak:
Unsupervised visualization of image datasets using contrastive learning. ICLR 2023 - [c15]Kerol R. Djoumessi Donteu, Indu Ilanchezian, Laura Kühlewein, Hanna Faber, Christian F. Baumgartner, Bubacarr Bah, Philipp Berens, Lisa M. Koch:
Sparse Activations for Interpretable Disease Grading. MIDL 2023: 1-17 - [i13]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
AI for Science: An Emerging Agenda. CoRR abs/2303.04217 (2023) - [i12]Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton, Philipp Berens:
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. CoRR abs/2303.04862 (2023) - [i11]Sebastian Damrich, Philipp Berens, Dmitry Kobak:
Persistent homology for high-dimensional data based on spectral methods. CoRR abs/2311.03087 (2023) - [i10]Indu Ilanchezian, Valentyn Boreiko, Laura Kühlewein, Ziwei Huang, Murat Seçkin Ayhan, Matthias Hein, Lisa M. Koch, Philipp Berens:
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models. CoRR abs/2311.11629 (2023) - 2022
- [j14]Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens:
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 50(4): 485-503 (2022) - [j13]Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens:
Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 51(3): 405 (2022) - [j12]Jan Niklas Böhm, Philipp Berens, Dmitry Kobak:
Attraction-Repulsion Spectrum in Neighbor Embeddings. J. Mach. Learn. Res. 23: 95:1-95:32 (2022) - [j11]Murat Seçkin Ayhan, Louis Benedikt Kümmerle, Laura Kühlewein, Werner Inhoffen, Gulnar Aliyeva, Focke Ziemssen, Philipp Berens:
Clinical validation of saliency maps for understanding deep neural networks in ophthalmology. Medical Image Anal. 77: 102364 (2022) - [c14]Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein:
Sparse Visual Counterfactual Explanations in Image Space. GCPR 2022: 133-148 - [c13]Valentyn Boreiko, Indu Ilanchezian, Murat Seçkin Ayhan, Sarah Müller, Lisa M. Koch, Hanna Faber, Philipp Berens, Matthias Hein:
Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images. MICCAI (2) 2022: 539-549 - [c12]Lisa M. Koch, Christian M. Schürch, Arthur Gretton, Philipp Berens:
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. MIDL 2022: 726-740 - [c11]Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens:
Efficient identification of informative features in simulation-based inference. NeurIPS 2022 - [c10]Rita González-Márquez, Philipp Berens, Dmitry Kobak:
Two-dimensional visualization of large document libraries using t-SNE. TAG-ML 2022: 133-141 - [i9]Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein:
Sparse Visual Counterfactual Explanations in Image Space. CoRR abs/2205.07972 (2022) - [i8]Sacha Sokoloski, Philipp Berens:
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering. CoRR abs/2206.04841 (2022) - [i7]Jan Niklas Böhm, Philipp Berens, Dmitry Kobak:
Unsupervised visualization of image datasets using contrastive learning. CoRR abs/2210.09879 (2022) - [i6]Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob H. Macke, Philipp Berens:
Efficient identification of informative features in simulation-based inference. CoRR abs/2210.11915 (2022) - [i5]Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Ulrike von Luxburg, Jessica Montgomery:
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). Dagstuhl Reports 12(9): 150-199 (2022) - 2021
- [c9]Sophie Laturnus, Philipp Berens:
MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space. ICML 2021: 6021-6031 - [c8]Indu Ilanchezian, Dmitry Kobak, Hanna Faber, Focke Ziemssen, Philipp Berens, Murat Seçkin Ayhan:
Interpretable Gender Classification from Retinal Fundus Images Using BagNets. MICCAI (3) 2021: 477-487 - [c7]Dominic Gonschorek, Larissa Höfling, Klaudia P. Szatko, Katrin Franke, Timm Schubert, Benjamin A. Dunn, Philipp Berens, David A. Klindt, Thomas Euler:
Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience. NeurIPS 2021: 3706-3719 - [i4]Ziwei Huang, Yanli Ran, Jonathan Oesterle, Thomas Euler, Philipp Berens:
Estimating smooth and sparse neural receptive fields with a flexible spline basis. CoRR abs/2108.07537 (2021) - 2020
- [j10]Sophie Laturnus, Adam von Daranyi, Ziwei Huang, Philipp Berens:
MorphoPy: A python package for feature extraction of neural morphologies. J. Open Source Softw. 5(52): 2339 (2020) - [j9]Murat Seçkin Ayhan, Laura Kühlewein, Gulnar Aliyeva, Werner Inhoffen, Focke Ziemssen, Philipp Berens:
Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. Medical Image Anal. 64: 101724 (2020) - [j8]Sophie Laturnus, Dmitry Kobak, Philipp Berens:
A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination. Neuroinformatics 18(4): 591-609 (2020) - [c6]Cornelius Schröder, David A. Klindt, Sarah Strauß, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens:
System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina. NeurIPS 2020 - [i3]Yves Bernaerts, Philipp Berens, Dmitry Kobak:
Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data. CoRR abs/2006.10411 (2020) - [i2]Jan Niklas Böhm, Philipp Berens, Dmitry Kobak:
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum. CoRR abs/2007.08902 (2020)
2010 – 2019
- 2019
- [j7]Luke E. Rogerson, Zhijian Zhao, Katrin Franke, Thomas Euler, Philipp Berens:
Bayesian hypothesis testing and experimental design for two-photon imaging data. PLoS Comput. Biol. 15(8) (2019) - [j6]Luke E. Rogerson, Zhijian Zhao, Katrin Franke, Thomas Euler, Philipp Berens:
Correction: Bayesian hypothesis testing and experimental design for two-photon imaging data. PLoS Comput. Biol. 15(10) (2019) - [c5]Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens:
Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. NeurIPS 2019: 7068-7078 - [c4]Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens:
Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations. ECML/PKDD (1) 2019: 124-139 - [i1]Dmitry Kobak, George C. Linderman, Stefan Steinerberger, Yuval Kluger, Philipp Berens:
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations. CoRR abs/1902.05804 (2019) - 2018
- [j5]Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick J. Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W. Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D. Harris, Ben Bolte, Timothy A. Machado, Dario Ringach, Jasmine Stone, Luke E. Rogerson, Nicolas J. Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S. Tolias, Matthias Bethge:
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14(5) (2018) - 2017
- [j4]Marcel Nonnenmacher, Christian Behrens, Philipp Berens, Matthias Bethge, Jakob H. Macke:
Signatures of criticality arise from random subsampling in simple population models. PLoS Comput. Biol. 13(10) (2017) - 2016
- [j3]Tom Baden, Philipp Berens, Katrin Franke, Miroslav Román Rosón, Matthias Bethge, Thomas Euler:
The functional diversity of retinal ganglion cells in the mouse. Nat. 529(7586): 345-350 (2016) - 2011
- [j2]Jakob H. Macke, Philipp Berens, Matthias Bethge:
Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data. Frontiers Comput. Neurosci. 5: 35 (2011)
2000 – 2009
- 2009
- [j1]Jakob H. Macke, Philipp Berens, Alexander S. Ecker, Andreas S. Tolias, Matthias Bethge:
Generating Spike Trains with Specified Correlation Coefficients. Neural Comput. 21(2): 397-423 (2009) - [c3]Philipp Berens, Sebastian Gerwinn, Alexander S. Ecker, Matthias Bethge:
Neurometric function analysis of population codes. NIPS 2009: 90-98 - [c2]Sebastian Gerwinn, Philipp Berens, Matthias Bethge:
A joint maximum-entropy model for binary neural population patterns and continuous signals. NIPS 2009: 620-628 - 2007
- [c1]Matthias Bethge, Philipp Berens:
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. NIPS 2007: 97-104
Coauthor Index
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