Giovanni Luca Marchetti

According to our database1, Giovanni Luca Marchetti authored at least 27 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Sequential Group Composition: A Window into the Mechanics of Deep Learning.
CoRR, February, 2026

Identifiable Equivariant Networks are Layerwise Equivariant.
CoRR, January, 2026

2025
Critical Points of Degenerate Metrics on Algebraic Varieties: A Tale of Overparametrization.
CoRR, December, 2025

Sprecher Networks: A Parameter-Efficient Kolmogorov-Arnold Architecture.
CoRR, December, 2025

Randomized HyperSteiner: A Stochastic Delaunay Triangulation Heuristic for the Hyperbolic Steiner Minimal Tree.
CoRR, October, 2025

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks.
CoRR, June, 2025

Learning on a Razor's Edge: the Singularity Bias of Polynomial Neural Networks.
CoRR, May, 2025

An Invitation to Neuroalgebraic Geometry.
CoRR, January, 2025

Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach.
Trans. Mach. Learn. Res., 2025

Relative Representations: Topological and Geometric Perspectives.
Proceedings of UniReps: the Second Edition of the Workshop on Unifying Representations in Neural Models, 2025

Position: Algebra Unveils Deep Learning - An Invitation to Neuroalgebraic Geometry.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Geometry of Lightning Self-Attention: Identifiability and Dimension.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

A Non-Adversarial Approach to Idempotent Generative Modelling.
Proceedings of the ECAI 2025 - 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy, 2025

HyperSteiner: Computing Heuristic Hyperbolic Steiner Minimal Trees.
Proceedings of the 27th Symposium on Algorithm Engineering and Experiments, 2025

On the Geometry and Optimization of Polynomial Convolutional Networks.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024
On Symmetries and Metrics in Geometric Inference.
PhD thesis, 2024

Hyperbolic Delaunay Geometric Alignment.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Harmonics of Learning: Universal Fourier Features Emerge in Invariant Networks.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023
Equivariant Representations for Non-Free Group Actions.
CoRR, 2023

Equivariant Representation Learning in the Presence of Stabilizers.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Learning Geometric Representations of Objects via Interaction.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Equivariant Representation Learning via Class-Pose Decomposition.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

An Efficient and Continuous Voronoi Density Estimator.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Voronoi density estimator for high-dimensional data: Computation, compactification and convergence.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Back to the Manifold: Recovering from Out-of-Distribution States.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Active Nearest Neighbor Regression Through Delaunay Refinement.
Proceedings of the International Conference on Machine Learning, 2022

2021
Learning Coarsened Dynamic Graph Representations for Deformable Object Manipulation.
Proceedings of the 20th International Conference on Advanced Robotics, 2021


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