3 citations · 15 across the 27 of their papers we have counts for
6 papers · 1 filter
Geometric organization of olfactory descriptor data in the Poincaré disk
Aniss Aiman Medbouhi, Farzaneh Taleb, Giovanni Luca Marchetti +1
Odor quality is commonly represented using high dimensional descriptor profiles, yet their low dimensional organization remains unclear. We investigated whether a two-dimensional h…
Linear Independence of Polynomial Compositions and Identifiability of Deep Neural Networks
Kathlén Kohn, Giovanni Luca Marchetti, Alex Massarenti +1
Motivated by theoretical problems in deep learning, we conjecture that post-composing a fixed number of pairwise distinct nonconstant polynomials with a generic polynomial of suffi…
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…
Sequential Group Composition: A Window into the Mechanics of Deep Learning
Giovanni Luca Marchetti, Daniel Kunin, Adele Myers +2
How do neural networks trained over sequences acquire the ability to perform structured operations, such as arithmetic, geometric, and algorithmic computation? To gain insight into…
Identifiable Equivariant Networks are Layerwise Equivariant
Vahid Shahverdi, Giovanni Luca Marchetti, Georg Bökman +1
We investigate the relation between end-to-end equivariance and layerwise equivariance in deep neural networks. We prove the following: For a network whose end-to-end function is e…