3 citations · 5 across the 7 of their papers we have counts for
18 papers
Singular Learning and Occam's Razor in Deep Monomial Networks
Kathlén Kohn, Giovanni Luca Marchetti, Farhan Shabir +2
In the optimization of neural networks, gradient dynamics are influenced by critical points that arise from the model's architecture. These critical points occur where the Jacobian…
MirrorDuo: Reflection-Consistent Visuomotor Learning from Mirrored Demonstration Pairs
Zheyu Zhuang, Ruiyu Wang, Giovanni Luca Marchetti +2
Image-based behaviour cloning leverages demonstrations captured from ubiquitous RGB cameras. However, it remains constrained by the cost of collecting diverse demos, especially for…
On the Geometry and Optimization of Polynomial Convolutional Networks
Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn
We study convolutional neural networks with monomial activation functions. Specifically, we prove that their parameterization map is regular and is an isomorphism almost everywhere…
An Efficient and Continuous Voronoi Density Estimator
Giovanni Luca Marchetti, Vladislav Polianskii, Anastasiia Varava +2
We introduce a non-parametric density estimator deemed Radial Voronoi Density Estimator (RVDE). RVDE is grounded in the geometry of Voronoi tessellations and as such benefits from…
Equivariant Representation Learning via Class-Pose Decomposition
Giovanni Luca Marchetti, Gustaf Tegnér, Anastasiia Varava +1
We introduce a general method for learning representations that are equivariant to symmetries of data. Our central idea is to decompose the latent space into an invariant factor an…
Learning on a Razor's Edge: Identifiability and Singularity of Polynomial Neural Networks
Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn
We study function spaces parametrized by neural networks, referred to as neuromanifolds. Specifically, we focus on deep Multi-Layer Perceptrons (MLPs) and Convolutional Neural Netw…