Arthur Jacot

According to our database, Arthur Jacot authored at least 25 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Shallow diffusion networks provably learn hidden low-dimensional structure.

Nicholas M. Boffi, Arthur Jacot, Stephen Tu, Ingvar M. Ziemann

CoRR, 2024

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse.

Arthur Jacot, Peter Súkeník, Zihan Wang, Marco Mondelli

CoRR, 2024

How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning.

Arthur Jacot, Seok Hoan Choi, Yuxiao Wen

CoRR, 2024

Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes.

Zhenfeng Tu, Santiago Aranguri, Arthur Jacot

CoRR, 2024

Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets.

Arthur Jacot, Alexandre Kaiser

CoRR, 2024

Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning.

Yuxiao Wen, Arthur Jacot

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Implicit bias of SGD in L2-regularized linear DNNs: One-way jumps from high to low rank.

Zihan Wang, Arthur Jacot

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Implicit bias of SGD in L₂-regularized linear DNNs: One-way jumps from high to low rank.

Zihan Wang, Arthur Jacot

CoRR, 2023

Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff.

Arthur Jacot

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions.

Arthur Jacot

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Feature Learning in L₂-regularized DNNs: Attraction/Repulsion and Sparsity.

Arthur Jacot, Eugene Golikov, Clément Hongler, Franck Gabriel

CoRR, 2022

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity.

Arthur Jacot, Eugene Golikov, Clément Hongler, Franck Gabriel

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Freeze and Chaos: NTK views on DNN Normalization, Checkerboard and Boundary Artifacts.

Arthur Jacot, Franck Gabriel, François Ged, Clément Hongler

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis.

Yatin Dandi, Arthur Jacot

CoRR, 2021

Deep Linear Networks Dynamics: Low-Rank Biases Induced by Initialization Scale and L2 Regularization.

Arthur Jacot, François Ged, Franck Gabriel, Berfin Simsek, Clément Hongler

CoRR, 2021

Neural tangent kernel: convergence and generalization in neural networks (invited paper).

Arthur Jacot, Franck Gabriel, Clément Hongler

Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel.

Benjamin Dupuis, Arthur Jacot

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances.

Berfin Simsek, François Ged, Arthur Jacot, Francesco Spadaro, Clément Hongler, Wulfram Gerstner, Johanni Brea

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Kernel Alignment Risk Estimator: Risk Prediction from Training Data.

Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Implicit Regularization of Random Feature Models.

Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, Franck Gabriel

Proceedings of the 37th International Conference on Machine Learning, 2020

The asymptotic spectrum of the Hessian of DNN throughout training.

Arthur Jacot, Franck Gabriel, Clément Hongler

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects.

Arthur Jacot, Franck Gabriel, Clément Hongler

CoRR, 2019

Disentangling feature and lazy learning in deep neural networks: an empirical study.

Mario Geiger, Stefano Spigler, Arthur Jacot, Matthieu Wyart

CoRR, 2019

Scaling description of generalization with number of parameters in deep learning.

Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d'Ascoli, Giulio Biroli, Clément Hongler, Matthieu Wyart

CoRR, 2019

2018

Neural Tangent Kernel: Convergence and Generalization in Neural Networks.

Arthur Jacot, Clément Hongler, Franck Gabriel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018