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Found 5 papers, 2 papers with code
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines.
Self-Supervised Graph Representation Learning for Neuronal Morphologies
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
Benchmarking Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding.
Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes.
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks.
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