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Creators/Authors contains: "Wolf, G."

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  1. ; ; ( , Advances in Intelligent Data Analysis)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ( , Advances in neural information processing systems)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; ( , Proceedings of the 40th International Conference on Machine Learning)
    Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the underlying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM’s utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells). 
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    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)