Search Results for author:
Found 5 papers, 2 papers with code
Hierarchical clustering with maximum density paths and mixture models
Hierarchical clustering is an effective and interpretable technique for analyzing structure in data, offering a nuanced understanding by revealing insights at multiple scales and resolutions.
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.
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Self-Supervised Graph Representation Learning for Neuronal Morphologies
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
