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Found 4 papers, 2 papers with code
TreeLearn: A Comprehensive 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.
