Near-Maximum Entropy Models for Binary Neural Representations of Natural Images. - Matthias Bethge; Philipp Berens (NIPS, 2007)

Neurometric function analysis of population codes. - Philipp Berens; Sebastian Gerwinn; Alexander S. Ecker; Matthias Bethge (NIPS, 2009)

A joint maximum-entropy model for binary neural population patterns and continuous signals. - Sebastian Gerwinn; Philipp Berens; Matthias Bethge (NIPS, 2009)

Generating Spike Trains with Specified Correlation Coefficients. - Jakob H. Macke; Philipp Berens; Alexander S. Ecker; Andreas S. Tolias; Matthias Bethge (Neural Comput., 2009)

Statistical Analysis of Multi-Cell Recordings: Linking Population Coding Models to Experimental Data. - Jakob H. Macke; Philipp Berens; Matthias Bethge (Frontiers Comput. Neurosci., 2011)

The functional diversity of retinal ganglion cells in the mouse. - Tom Baden; Philipp Berens; Katrin Franke; Miroslav Román Rosón; Matthias Bethge; Thomas Euler (Nat., 2016)

Signatures of criticality arise from random subsampling in simple population models. - Marcel Nonnenmacher; Christian Behrens; Philipp Berens; Matthias Bethge; Jakob H. Macke (PLoS Comput. Biol., 2017)

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. - 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; et al. (PLoS Comput. Biol., 2018)

Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse. - Cornelius Schröder; Ben James; Leon Lagnado; Philipp Berens (NeurIPS, 2019)

Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisations. - Dmitry Kobak; George C. Linderman; Stefan Steinerberger; Yuval Kluger; Philipp Berens (ECML/PKDD (1), 2019)

Bayesian hypothesis testing and experimental design for two-photon imaging data. - Luke E. Rogerson; Zhijian Zhao; Katrin Franke; Thomas Euler; Philipp Berens (PLoS Comput. Biol., 2019)

Correction: Bayesian hypothesis testing and experimental design for two-photon imaging data. - Luke E. Rogerson; Zhijian Zhao; Katrin Franke; Thomas Euler; Philipp Berens (PLoS Comput. Biol., 2019)

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina. - Cornelius Schröder; David A. Klindt; Sarah Strauß; Katrin Franke; Matthias Bethge; Thomas Euler; Philipp Berens (NeurIPS, 2020)

Sparse Bottleneck Networks for Exploratory Analysis and Visualization of Neural Patch-seq Data. - Yves Bernaerts; Philipp Berens; Dmitry Kobak (CoRR, 2020)

A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (CoRR, 2020)

MorphoPy: A python package for feature extraction of neural morphologies. - Sophie Laturnus; Adam von Daranyi; Ziwei Huang; Philipp Berens (J. Open Source Softw., 2020)

Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection. - Murat Seçkin Ayhan; Laura Kühlewein; Gulnar Aliyeva; Werner Inhoffen; Focke Ziemssen; Philipp Berens (Medical Image Anal., 2020)

A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination. - Sophie Laturnus; Dmitry Kobak; Philipp Berens (Neuroinformatics, 2020)

MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space. - Sophie Laturnus; Philipp Berens (ICML, 2021)

Interpretable Gender Classification from Retinal Fundus Images Using BagNets. - Indu Ilanchezian; Dmitry Kobak; Hanna Faber; Focke Ziemssen; Philipp Berens; Murat Seçkin Ayhan (MICCAI (3), 2021)

Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience. - Dominic Gonschorek; Larissa Höfling; Klaudia P. Szatko; Katrin Franke; Timm Schubert; Benjamin A. Dunn; Philipp Berens; David A. Klindt; Thomas Euler (NeurIPS, 2021)

Estimating smooth and sparse neural receptive fields with a flexible spline basis. - Ziwei Huang; Yanli Ran; Jonathan Oesterle; Thomas Euler; Philipp Berens (CoRR, 2021)

Sparse Visual Counterfactual Explanations in Image Space. - Valentyn Boreiko; Maximilian Augustin; Francesco Croce; Philipp Berens; Matthias Hein 001 (GCPR, 2022)

Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images. - Valentyn Boreiko; Indu Ilanchezian; Murat Seçkin Ayhan; Sarah Müller; Lisa M. Koch; Hanna Faber; Philipp Berens; Matthias Hein 001 (MICCAI (2), 2022)

Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. - Lisa M. Koch; Christian M. Schürch; Arthur Gretton; Philipp Berens (MIDL, 2022)

Efficient identification of informative features in simulation-based inference. - Jonas Beck; Michael Deistler; Yves Bernaerts; Jakob H. Macke; Philipp Berens (NeurIPS, 2022)

Two-dimensional visualization of large document libraries using t-SNE. - Rita González-Márquez; Philipp Berens; Dmitry Kobak (TAG-ML, 2022)

Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering. - Sacha Sokoloski; Philipp Berens (CoRR, 2022)

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. - Jonathan Oesterle; Nicholas Krämer; Philipp Hennig; Philipp Berens (J. Comput. Neurosci., 2022)

Attraction-Repulsion Spectrum in Neighbor Embeddings. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (J. Mach. Learn. Res., 2022)

Clinical validation of saliency maps for understanding deep neural networks in ophthalmology. - Murat Seçkin Ayhan; Louis Benedikt Kümmerle; Laura Kühlewein; Werner Inhoffen; Gulnar Aliyeva; Focke Ziemssen; Philipp Berens (Medical Image Anal., 2022)

Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. - Jonathan Oesterle; Nicholas Krämer; Philipp Hennig; Philipp Berens (J. Comput. Neurosci., 2022)

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382). - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (Dagstuhl Reports, 2022)

Unsupervised visualization of image datasets using contrastive learning. - Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (ICLR, 2023)

Sparse Activations for Interpretable Disease Grading. - Kerol R. Djoumessi Donteu; Indu Ilanchezian; Laura Kühlewein; Hanna Faber; Christian F. Baumgartner; Bubacarr Bah; Philipp Berens; Lisa M. Koch (MIDL, 2023)

AI for Science: An Emerging Agenda. - Philipp Berens; Kyle Cranmer; Neil D. Lawrence; Ulrike von Luxburg; Jessica Montgomery (CoRR, 2023)

Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. - Lisa M. Koch; Christian M. Schürch; Christian F. Baumgartner; Arthur Gretton; Philipp Berens (CoRR, 2023)

Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models. - Indu Ilanchezian; Valentyn Boreiko; Laura Kühlewein; Ziwei Huang; Murat Seçkin Ayhan; Matthias Hein 001; Lisa M. Koch; Philipp Berens (CoRR, 2023)

Most discriminative stimuli for functional cell type clustering. - 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; et al. (ICLR, 2024)

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. - Jonas Beck; Nathanael Bosch; Michael Deistler; Kyra L. Kadhim; Jakob H. Macke; Philipp Hennig; Philipp Berens (ICML, 2024)

This Actually Looks Like that: Proto-BagNets for Local and Global Interpretability-by-Design. - Kerol Djoumessi; Bubacarr Bah; Laura Kühlewein; Philipp Berens; Lisa M. Koch (MICCAI (10), 2024)

Interpretable-by-Design Deep Survival Analysis for Disease Progression Modeling. - Julius Gervelmeyer; Sarah Müller; Kerol Djoumessi; David Merle; Simon J. Clark; Lisa M. Koch; Philipp Berens (MICCAI (10), 2024)

Efficiently correcting patch-based segmentation errors to control image-level performance in retinal images. - Patrick Köhler; Jeremiah Fadugba; Philipp Berens; Lisa M. Koch (MIDL, 2024)

Leveraging Probabilistic Segmentation Models for Improved Glaucoma Diagnosis: A Clinical Pipeline Approach. - Anna M. Wundram; Paul Fischer; Stephan Wunderlich; Hanna Faber; Lisa M. Koch; Philipp Berens; Christian F. Baumgartner (MIDL, 2024)

Benchmarking Dependence Measures to Prevent Shortcut Learning in Medical Imaging. - Sarah Müller; Louisa Fay; Lisa M. Koch; Sergios Gatidis; Thomas Küstner; Philipp Berens (MLMI@MICCAI (2), 2024)

Persistent Homology for High-dimensional Data Based on Spectral Methods. - Sebastian Damrich; Philipp Berens; Dmitry Kobak (NeurIPS, 2024)

Most discriminative stimuli for functional cell type identification. - 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; et al. (CoRR, 2024)

A computational approach to visual ecology with deep reinforcement learning. - Sacha Sokoloski; Jure Majnik; Philipp Berens (CoRR, 2024)

Self-supervised Visualisation of Medical Image Datasets. - Ifeoma Veronica Nwabufo; Jan Niklas Böhm; Philipp Berens; Dmitry Kobak (CoRR, 2024)

Estimating Causal Effects with Double Machine Learning - A Method Evaluation. - Jonathan Fuhr; Philipp Berens; Dominik Papies (CoRR, 2024)

Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization. - Jeremiah Fadugba; Patrick Köhler; Lisa M. Koch; Petru Manescu; Philipp Berens (CoRR, 2024)

Foundation models in healthcare require rethinking reliability. - Thomas Grote; Timo Freiesleben; Philipp Berens (Nat. Mac. Intell., 2024)

Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study. - Lisa M. Koch; Christian F. Baumgartner; Philipp Berens (npj Digit. Medicine, 2024)

Meet the authors: Rita González-Márquez, Philipp Berens, and Dmitry Kobak. - Rita González-Márquez; Philipp Berens; Dmitry Kobak (Patterns, 2024)

The landscape of biomedical research. - Rita González-Márquez; Luca Schmidt; Benjamin M. Schmidt; Philipp Berens; Dmitry Kobak (Patterns, 2024)

The art of seeing the elephant in the room: 2D embeddings of single-cell data do make sense. - Jan Lause; Philipp Berens; Dmitry Kobak (PLoS Comput. Biol., 2024)

Disentangling representations of retinal images with generative models. - Sarah Müller; Lisa M. Koch; Hendrik P. A. Lensch; Philipp Berens (Medical Image Anal., 2025)

Learning Disease State from Noisy Ordinal Disease Progression Labels. - Gustav Schmidt; Holger Heidrich; Philipp Berens; Sarah Müller (CoRR, 2025)

A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification. - Kerol Djoumessi; Samuel Ofosu Mensah; Philipp Berens (CoRR, 2025)

Fundus Image Toolbox: A Python package for fundus image processing. - Julius Gervelmeyer; Sarah Müller; Ziwei Huang; Philipp Berens (J. Open Source Softw., 2025)

Soft-CAM: Making black box models self-explainable for high-stakes decisions. - Kerol Djoumessi; Philipp Berens (CoRR, 2025)

Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning. - Samuel Ofosu Mensah; Kerol Djoumessi; Philipp Berens (CoRR, 2025)