5 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
Training-Free Transformer Merging via Sequential Local Operator Alignment
Akansh Maurya, Ya-Wei Eileen Lin, Stefanie Jegelka +2
Training-free model merging aims to combine multiple fine-tuned models into a single model without further optimization on labeled data. Yet, in transformers, independently merging…
Joint Hierarchical Representation Learning of Samples and Features via Informed Tree-Wasserstein Distance
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
High-dimensional data often exhibit hierarchical structures in both modes: samples and features. Yet, most existing approaches for hierarchical representation learning consider onl…
Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimension…
Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
Ya-Wei Eileen Lin, Ronen Talmon, Ron Levie
Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving g…
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne +1
Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Ou…