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

29 September 2022

Papers citing "Implicit Bias of Large Depth Networks: a Notion of Rank for Nonlinear Functions"

9 / 9 papers shown

Title
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks Chenyang Zhang Peifeng Gao Difan Zou Yuan Cao OOD MLT 68 0 0 11 Apr 2025
Explainable Neural Networks with Guarantees: A Sparse Estimation Approach Antoine Ledent Peng Liu FAtt 109 0 0 20 Feb 2025
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries Chris Kolb T. Weber Bernd Bischl David Rügamer 113 0 0 04 Feb 2025
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning Arthur Jacot Seok Hoan Choi Yuxiao Wen AI4CE 94 2 0 08 Jul 2024
Hamiltonian Mechanics of Feature Learning: Bottleneck Structure in Leaky ResNets Arthur Jacot Alexandre Kaiser 38 0 0 27 May 2024
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning Yuxiao Wen Arthur Jacot 63 6 0 12 Feb 2024
Bottleneck Structure in Learned Features: Low-Dimension vs Regularity Tradeoff Arthur Jacot MLT 26 13 0 30 May 2023
ReLU Neural Networks with Linear Layers are Biased Towards Single- and Multi-Index Models Suzanna Parkinson Greg Ongie Rebecca Willett 68 6 0 24 May 2023
The Role of Linear Layers in Nonlinear Interpolating Networks Greg Ongie Rebecca Willett 66 15 0 02 Feb 2022