Neural Tangent Kernel: Convergence and Generalization in Neural Networks. - Arthur Jacot; Clément Hongler; Franck Gabriel (NeurIPS, 2018)

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)

Disentangling feature and lazy learning in deep neural networks: an empirical study. - Mario Geiger; Stefano Spigler; Arthur Jacot; Matthieu Wyart (CoRR, 2019)

Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects. - Arthur Jacot; Franck Gabriel; Clément Hongler (CoRR, 2019)

The asymptotic spectrum of the Hessian of DNN throughout training. - Arthur Jacot; Franck Gabriel; Clément Hongler (ICLR, 2020)

Implicit Regularization of Random Feature Models. - Arthur Jacot; Berfin Simsek; Francesco Spadaro; Clément Hongler; Franck Gabriel (ICML, 2020)

Kernel Alignment Risk Estimator: Risk Prediction from Training Data. - Arthur Jacot; Berfin Simsek; Francesco Spadaro; Clément Hongler; Franck Gabriel (NeurIPS, 2020)

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 (ICML, 2021)

DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel. - Benjamin Dupuis; Arthur Jacot (NeurIPS, 2021)

Neural tangent kernel: convergence and generalization in neural networks (invited paper). - Arthur Jacot; Franck Gabriel; Clément Hongler (STOC, 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)

Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis. - Yatin Dandi; Arthur Jacot (CoRR, 2021)

Freeze and Chaos: NTK views on DNN Normalization, Checkerboard and Boundary Artifacts. - Arthur Jacot; Franck Gabriel; François Ged; Clément Hongler (MSML, 2022)

Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity. - Arthur Jacot; Eugene A. Golikov; Clément Hongler; Franck Gabriel (NeurIPS, 2022)

Feature Learning in L2-regularized DNNs: Attraction/Repulsion and Sparsity. - Arthur Jacot; Eugene A. Golikov; Clément Hongler; Franck Gabriel (CoRR, 2022)

Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions. - Arthur Jacot (ICLR, 2023)

Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff. - Arthur Jacot (NeurIPS, 2023)

Implicit bias of SGD in L2-regularized linear DNNs: One-way jumps from high to low rank. - Zihan Wang; Arthur Jacot (CoRR, 2023)

Implicit bias of SGD in L2-regularized linear DNNs: One-way jumps from high to low rank. - Zihan Wang; Arthur Jacot (ICLR, 2024)

Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning. - Yuxiao Wen; Arthur Jacot (ICML, 2024)

Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes. - Zhenfeng Tu; Santiago Aranguri; Arthur Jacot (NeurIPS, 2024)

Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets. - Arthur Jacot; Alexandre Kaiser (CPAL, 2025)

Shallow diffusion networks provably learn hidden low-dimensional structure. - Nicholas Matthew Boffi; Arthur Jacot; Stephen Tu; Ingvar M. Ziemann (ICLR, 2025)

How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning. - Arthur Jacot; Seok Hoan Choi; Yuxiao Wen (ICLR, 2025)

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. - Arthur Jacot; Peter Súkeník; Zihan Wang; Marco Mondelli (ICLR, 2025)

Saddle-To-Saddle Dynamics in Deep ReLU Networks: Low-Rank Bias in the First Saddle Escape. - Ioannis Bantzis; James B. Simon; Arthur Jacot (CoRR, 2025)