6 papers
IdEst: Assessing Self-Supervised Learning Representations via Intrinsic Dimension
Julie Mordacq, Vicky Kalogeiton, Steve Oudot
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning meaningful representations from unlabeled data. However, the standard protocol for evaluating these r…
Estimating the persistent homology of -valued functions using function-geometric multifiltrations
Ethan André, Jingyi Li, David Loiseaux +1
Given an unknown -valued function on a metric space , can we approximate the persistent homology of from a finite sampling of with known pairwise dista…
T-REGS: Minimum Spanning Tree Regularization for Self-Supervised Learning
Julie Mordacq, David Loiseaux, Vicky Kalogeiton +1
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data, often by enforcing invariance to input transformations such as…
Counts and end-curves in two-parameter persistence
Thomas Brüstle, Steve Oudot, Luis Scoccola +1
Given a finite dimensional, bigraded module over the polynomial ring in two variables, we define its two-parameter count, a natural number, and its end-curves, a set of plane curve…
D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
Soham Mukherjee, Shreyas N. Samaga, Cheng Xin +2
End-to-end topological learning using 1-parameter persistence is well-known. We show that the framework can be enhanced using 2-parameter persistence by adopting a recently introdu…
Local characterization of block-decomposability for multiparameter persistence modules
Vadim Lebovici, Jan-Paul Lerch, Steve Oudot
Local conditions for the direct summands of a persistence module to belong to a certain class of indecomposables have been proposed in the 2-parameter setting, notably for the clas…