Search Results for author:
Found 7 papers, 3 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.
FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
Reconstructing the 3D location and size of microparticles from diffraction images - holograms - is a computationally expensive inverse problem that has traditionally been solved using physics-based reconstruction methods.
Reproducibility of predictive networks for mouse visual cortex
Such weight vectors, which can be thought as embeddings of neuronal function, have been proposed to define functional cell types via unsupervised clustering.
TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds
We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments.
Assessing out-of-domain generalization for robust building damage detection
Such models operate in a multi-domain setting: every disaster is inherently different (new geolocation, unique circumstances), and models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event.
Neurometric function analysis of population codes
In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding.
