4 citations · 7 across the 5 of their papers we have counts for
5 papers
Path-independent Flow Matching for Multi-parameter Generative Dynamics
Francisco Téllez, AmirHossein Zamani, Philippe Martin +5
Flow Matching is a powerful framework for learning transport maps between probability distributions. Yet its standard single-parameter formulation is not designed to capture multi-…
scShapeBench: Discovering geometry from high dimensional scRNAseq data
Andrew J Steindl, João Felipe Rocha, Brian Tshilengi Di Bassinga +13
High-dimensional point cloud data arise across many scientific domains, especially single-cell biology. The shapes or topologies of these datasets determine the types of informatio…
MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data
Xingzhi Sun, João Felipe Rocha, Brett Phelan +11
Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A key challenge is inferring continu…
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun, Danqi Liao, Kincaid MacDonald +7
Rapid growth of high-dimensional datasets in fields such as single-cell RNA sequencing and spatial genomics has led to unprecedented opportunities for scientific discovery, but it…
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Danqi Liao, Chen Liu, Benjamin W. Christensen +6
Entropy and mutual information in neural networks provide rich information on the learning process, but they have proven difficult to compute reliably in high dimensions. Indeed, i…