This paper presents varifold learning, a learning framework based on the mathematical concept of varifolds. Different from manifold based methods, our varifold learning framework ...
Maintaining statistics on multidimensional data distributions is crucial for predicting the run-time and result size of queries and data analysis tasks with acceptable accuracy. To...
We propose a novel denoising algorithm to reduce the Poisson noise that is typically dominant in fluorescence microscopy data. To process large datasets at a low computational co...
Owing to the stochastic nature of discrete processes such as photon counts in imaging, a variety of real-world data are well modeled as Poisson random variables whose means are in...
This paper presents the basics of a new paradigm that allows generators and consumers of global contextual information to determine an appropriate security level needed for contex...