$Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias$

Intrinsic dimensionality and generalization properties of the $\mathcal{R}$ -norm inductive bias

10 June 2022

Clayton Sanford

Papers citing "Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias"

10 / 10 papers shown

Title
The Effects of Multi-Task Learning on ReLU Neural Network Functions Julia B. Nakhleh Joseph Shenouda Robert D. Nowak 39 1 0 29 Oct 2024
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product Neil Rohit Mallinar Daniel Beaglehole Libin Zhu Adityanarayanan Radhakrishnan Parthe Pandit Misha Belkin 51 7 0 29 Jul 2024
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
Penalising the biases in norm regularisation enforces sparsity Etienne Boursier Nicolas Flammarion 40 14 0 02 Mar 2023
Learning Single-Index Models with Shallow Neural Networks A. Bietti Joan Bruna Clayton Sanford M. Song 170 68 0 27 Oct 2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini Sejun Park M. Girotti Ioannis Mitliagkas Murat A. Erdogdu MLT 324 48 0 29 Sep 2022
Ridgeless Interpolation with Shallow ReLU Networks in $1D$ is Nearest Neighbor Curvature Extrapolation and Provably Generalizes on Lipschitz Functions Boris Hanin MLT 38 9 0 27 Sep 2021
Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks Rahul Parhi Robert D. Nowak 56 38 0 18 Sep 2021
Benefits of depth in neural networks Matus Telgarsky 151 602 0 14 Feb 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015