75 citations · 88 across the 11 of their papers we have counts for
4 papers · 1 filter
Loss-Invariant Projections as Passive Probes of Learned Representations
Akshay Chandrasekhar, Pavlo Melnyk
Learned feature representations in neural networks often contain structure beyond that directly used by the final task output. We study this structure using $\textit{passive probes…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
O Learning Deep O()-Equivariant Hyperspheres
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck +2
In this paper, we utilize hyperspheres and regular -simplexes and propose an approach to learning deep features equivariant under the transformations of D reflections and rot…
Embed Me If You Can: A Geometric Perceptron
Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck
Solving geometric tasks involving point clouds by using machine learning is a challenging problem. Standard feed-forward neural networks combine linear or, if the bias parameter is…