2 citations · 5 across the 8 of their papers we have counts for
10 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…
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…
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…
Computation of -linear projected barcodes for multiparameter persistence
Alex Fernandes, Steve Oudot, Francois Petit
The -linear projected barcode was recently introduced as an alternative to the well-known fibered barcode for multiparameter persistence, in which restrictions of the modules to…
ADAPT: Multimodal Learning for Detecting Physiological Changes under Missing Modalities
Julie Mordacq, Leo Milecki, Maria Vakalopoulou +2
Multimodality has recently gained attention in the medical domain, where imaging or video modalities may be integrated with biomedical signals or health records. Yet, two challenge…
Differentiability and Optimization of Multiparameter Persistent Homology
Luis Scoccola, Siddharth Setlur, David Loiseaux +2
Real-valued functions on geometric data -- such as node attributes on a graph -- can be optimized using descriptors from persistent homology, allowing the user to incorporate topol…
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…
Efficient computation of topological integral transforms
Vadim Lebovici, Steve Oudot, Hugo Passe
Topological integral transforms have found many applications in shape analysis, from prediction of clinical outcomes in brain cancer to analysis of barley seeds. Using Euler charac…
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…