Search Results for author:
Found 5 papers, 4 papers with code
Let's do the time-warp-attend: Learning topological invariants of dynamical systems
Our method provides valuable insights into the qualitative, long-term behavior of a wide range of dynamical systems, and can detect bifurcations or catastrophic transitions in large-scale physical and biological systems.
scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching
Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context.
Phase2vec: Dynamical systems embedding with a physics-informed convolutional network
In an auxiliary training period, embeddings are optimized so that they robustly encode the equations of unseen data over and above the performance of a per-equation fitting method.
Unified framework for modeling multivariate distributions in biological sequences
Revealing the functional sites of biological sequences, such as evolutionary conserved, structurally interacting or co-evolving protein sites, is a fundamental, and yet challenging task.
Quantitative Methods
Statistical Optimal Transport via Factored Couplings
We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension.
