3 citations · 6 across the 10 of their papers we have counts for
14 papers
Retargeting Motions to Diverse Skeletons via Learnable Flattening
Kia-Jüng Yang, Fabian H. Sinz, Paweł A. Pierzchlewicz
Cross-structural motion retargeting aims to transfer motion between different skeletal topologies. Despite recent progress, existing state-of-the-art models struggle with reliabili…
Monte Carlo Event Generation with Continuous Normalizing Flows
Enrico Bothmann, Timo Janßen, Max Knobbe +2
We apply Continuous Normalizing Flows trained with the Flow Matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics.…
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert +18
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains un…
Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture
Seth Donahue, Irina Djuraskovic, Kunal Shah +3
Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerle…
Beyond Pixels: A Differentiable Pipeline for Probing Neuronal Selectivity in 3D
Pavithra Elumalai, Mohammad Bashiri, Goirik Chakrabarty +2
Visual perception relies on inference of 3D scene properties such as shape, pose, and lighting. To understand how visual sensory neurons enable robust perception, it is crucial to…
Efficient many-jet event generation with Flow Matching
Enrico Bothmann, Timo Janßen, Max Knobbe +2
We apply for the first time the Flow Matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the…
Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
Sara Rajaram, R. James Cotton, Fabian H. Sinz
Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most pr…
TRACE: Contrastive learning for multi-trial time-series data in neuroscience
Lisa Schmors, Dominic Gonschorek, Jan Niklas Böhm +9
Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Co…
Biomechanical Reconstruction with Confidence Intervals from Multiview Markerless Motion Capture
R. James Cotton, Fabian Sinz
Advances in multiview markerless motion capture (MMMC) promise high-quality movement analysis for clinical practice and research. While prior validation studies show MMMC performs…
Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors
Finn Schmidt, Polina Turishcheva, Suhas Shrinivasan +1
The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. C…
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
Reproducibility of predictive networks for mouse visual cortex
Polina Turishcheva, Max Burg, Fabian H. Sinz +1
Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a…
Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation
Paweł A. Pierzchlewicz, Caio O. da Silva, R. James Cotton +1
Single camera 3D pose estimation is an ill-defined problem due to inherent ambiguities from depth, occlusion or keypoint noise. Multi-hypothesis pose estimation accounts for this u…
Adversarial Distribution Balancing for Counterfactual Reasoning
Stefan Schrod, Fabian Sinz, Michael Altenbuchinger
The development of causal prediction models is challenged by the fact that the outcome is only observable for the applied (factual) intervention and not for its alternatives (the s…