2 citations · 3 across the 6 of their papers we have counts for
6 papers
Hyperbolic Delaunay Geometric Alignment
Aniss Aiman Medbouhi, Giovanni Luca Marchetti, Vladislav Polianskii +4
Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resu…
A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models
Nils Ingelhag, Jesper Munkeby, Jonne van Haastregt +3
In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obta…
Delaunay Component Analysis for Evaluation of Data Representations
Petra Poklukar, Vladislav Polianskii, Anastasia Varava +2
Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological anal…
GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs
Ciwan Ceylan, Petra Poklukar, Hanna Hultin +3
We argue that when comparing two graphs, the distribution of node structural features is more informative than global graph statistics which are often used in practice, especially…
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Constantinos Chamzas, Martina Lippi, Michael C. Welle +3
Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the rec…
GeomCA: Geometric Evaluation of Data Representations
Petra Poklukar, Anastasia Varava, Danica Kragic
Evaluating the quality of learned representations without relying on a downstream task remains one of the challenges in representation learning. In this work, we present Geometric…