3 papers
cs.CV2024
Analyzing Local Representations of Self-supervised Vision Transformers
Ani Vanyan, Alvard Barseghyan, Hakob Tamazyan +3
In this paper, we present a comparative analysis of various self-supervised Vision Transformers (ViTs), focusing on their local representative power. Inspired by large language mod…
cs.HC2023
Balancing between the Local and Global Structures (LGS) in Graph Embedding
Jacob Miller, Vahan Huroyan, Stephen Kobourov
We present a method for balancing between the Local and Global Structures (LGS) in graph embedding, via a tunable parameter. Some embedding methods aim to capture global structures…
cs.CG2022
Spherical Graph Drawing by Multi-dimensional Scaling
Jacob Miller, Vahan Huroyan, Stephen Kobourov
We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to s…