Publications

2027

Mines
Geostatistical Discrete Rate Simulation of Coupled Mine, Processing, Tailings, and Backfill Systems
Arthur Ayestas Hilgert, Alessandro Navarra

42nd International Symposium on the Application of Computers and Operations Research in the Mineral Industry (APCOM 2027), Montréal Accepté

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2026

Interprétabilité
Investigating what neural networks learn to fit SO(3) multiplication
Arthur Ayestas Hilgert, Xiangzhuo Zeng, Gabriela Moisescu-Pareja, Zhaoyue Wang, Sihui Wei, Vincent Létourneau, Gavin McCracken

Workshop on Unifying Representations in Neural Models (UniReps 2026) En cours d'évaluation

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Interprétabilité
Interpreting networks learning the Sporadic Group M₁₁ multiplication
Zhaoyue Wang, Arthur Ayestas Hilgert, Xiangzhuo Zeng, Gabriela Moisescu-Pareja, Sihui Wei, Gavin McCracken

Workshop on Unifying Representations in Neural Models (UniReps 2026) En cours d'évaluation

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Interprétabilité
Interpreting SO(n) Multiplication: Deep Networks Generalize by Learning an Algorithm
Arthur Ayestas Hilgert, Xiangzhuo Zeng, Sihui Wei, Gabriela Moisescu-Pareja, Vincent Létourneau, Gavin McCracken

International Conference on Learning Representations (ICLR 2027) En cours d'évaluation

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Interprétabilité
The Form of the Weights in Deep Networks Trained on Modular Addition
Sihui Wei, Arthur Ayestas Hilgert, Xiangzhuo Zeng, Gavin McCracken

International Conference on Learning Representations (ICLR 2027) En cours d'évaluation

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Interprétabilité
Deep neural networks divide and conquer dihedral multiplication
Sihui Wei, Gavin McCracken, Gabriela Moisescu-Pareja, Harley Wiltzer, Doina Precup, Irina Rish, Jonathan Love

International Conference on Machine Learning (ICML 2026)

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Interprétabilité
On the Geometry and Topology of Representations: The Manifolds of Modular Addition
Gabriela Moisescu-Pareja, Gavin McCracken, Harley Wiltzer, Vincent Létourneau, Colin Daniels, Doina Precup, Jonathan Love

International Conference on Learning Representations (ICLR 2026)

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Interprétabilité
Toward a general understanding of neural representations learned by deep neural networks on group multiplications
Arthur Ayestas Hilgert, Sihui Wei, Doina Precup, Gabriela Moisescu-Pareja, Gavin McCracken

Topology, Algebra, and Geometry in Data Science (TAG-DS 2026) Présentation orale

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2025

Interprétabilité
Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks
Gavin McCracken, Gabriela Moisescu-Pareja, Vincent Létourneau, Doina Precup, Jonathan Love

Neural Information Processing Systems (NeurIPS 2025)

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Interprétabilité
Interpreting deep neural networks trained on elementary p groups reveals algorithmic structure
Gavin McCracken, Arthur Ayestas Hilgert, Sihui Wei, Gabriela Moisescu-Pareja, Zhaoyue Wang, Jonathan Love

Topology, Algebra, and Geometry in Data Science (TAG-DS 2025) Présentation éclair

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Interprétabilité
The Geometry and Topology of Modular Addition Representations
Gabriela Moisescu-Pareja, Gavin McCracken, Harley Wiltzer, Vincent Létourneau, Colin Daniels, Doina Precup, Jonathan Love

Topology, Algebra, and Geometry in Data Science (TAG-DS 2025)

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Interprétabilité
The Representations of Deep Neural Networks Trained on Dihedral Group Multiplication
Gavin McCracken, Sihui Wei, Gabriela Moisescu-Pareja, Harley Wiltzer, Irina Rish, Jonathan Love

NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations

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