2 citations · 4 across the 4 of their papers we have counts for
8 papers
Convex Potential Learning for Optimal Transport Between Medical Images
Hongyu Kan, Ivan Medri, Natasha Ironside +4
Computing optimal transport maps between medical images plays a fundamental role in morphological analysis. However, existing methods are either difficult to scale to large 3D medi…
Confine to the Task, Anchor the Rest: Concept-Guided Retention Regularizer for Parameter-Efficient Fine-Tuning
Ibna Kowsar, Ivan Medri, Fanyu Meng +1
Parameter-efficient fine-tuning (PEFT) has been widely used, yet the adapted model suffers catastrophic forgetting of pre-trained knowledge. While most existing work hardens the ad…
FlowLOT: Linearized Optimal Transport for Flow Cytometry Analysis
Naqib Sad Pathan, Mohammad Shifat-E-Rabbi, Kristofor E. Pas +3
Multiparameter flow cytometry generates high-dimensional, unordered single-cell mea- surement data for disease diagnosis and monitoring, yet analysis often remains dependent on man…
3D Transport-based Morphometry (3D-TBM) for medical image analysis
Hongyu Kan, Kristofor Pas, Ivan Medri +5
Transport-Based Morphometry (TBM) has emerged as a new framework for 3D medical image analysis. By embedding images into a transport domain via invertible transformations, TBM faci…
Gromov-Wasserstein Barycenters: The Analysis Problem
Rocío Díaz Martín, Ivan V. Medri, James M. Murphy
This paper considers the problem of estimating a matrix that encodes pairwise distances in a finite metric space (or, more generally, the edge weight matrix of a network) under the…
Data representation with optimal transport
Rocío Díaz Martín, Ivan V. Medri, Gustavo Kunde Rohde
Optimal transport has been used to define bijective nonlinear transforms and different transport-related metrics for discriminating data and signals. Here we briefly describe the a…
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking
Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad
Despite groundbreaking success in image and text learning, deep learning has not achieved significant improvements against traditional machine learning (ML) when it comes to tabula…
Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning
Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad
Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneou…