133 citations · 143 across the 7 of their papers we have counts for
7 papers
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…
Hierarchical clustering with maximum density paths and mixture models
Martin Ritzert, Polina Turishcheva, Laura Hansel +3
Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clust…
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Polina Turishcheva, Laura Hansel, Martin Ritzert +2
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used t…
Self-Supervised Graph Representation Learning for Neuronal Morphologies
Marissa A. Weis, Laura Hansel, Timo Lüddecke +1
Unsupervised graph representation learning has recently gained interest in several application domains such as neuroscience, where modeling the diverse morphology of cell types in…
Benchmarking Unsupervised Object Representations for Video Sequences
Marissa A. Weis, Kashyap Chitta, Yash Sharma +4
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been propose…
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled…
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys +2
Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by findi…