1 citations · 2 across the 8 of their papers we have counts for
8 papers
A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks
Mike Nguyen, Nicole Mücke
We study data-driven early stopping for spectral regularisation methods in the classical non-parametric regression setting. Building on the discrepancy principle, we propose a mult…
Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators
Mike Nguyen, Nicole Mücke
In this work, we investigate the generalization properties of random feature methods. Our analysis extends prior results for Tikhonov regularization to a broad class of spectral re…
Random feature approximation for general spectral methods
Mike Nguyen, Nicole Mücke
Random feature approximation is arguably one of the most widely used techniques for kernel methods in large-scale learning algorithms. In this work, we analyze the generalization p…
Optimal Convergence Rates for Neural Operators
Mike Nguyen, Nicole Mücke
We introduce the neural tangent kernel (NTK) regime for two-layer neural operators and analyze their generalization properties. For early-stopped gradient descent (GD), we derive f…
Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping
Kevin Zhang, Luka Chkhetiani, Francis McCann Ramirez +10
This paper presents Conformer-1, an end-to-end Automatic Speech Recognition (ASR) model trained on an extensive dataset of 570k hours of speech audio data, 91% of which was acquire…
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent
Mike Nguyen, Nicole Mücke
We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive…
Random feature approximation for general spectral methods
Mike Nguyen, Nicole Mücke
Random feature approximation is arguably one of the most popular techniques to speed up kernel methods in large scale algorithms and provides a theoretical approach to the analysis…
Local SGD in Overparameterized Linear Regression
Mike Nguyen, Charly Kirst, Nicole Mücke
We consider distributed learning using constant stepsize SGD (DSGD) over several devices, each sending a final model update to a central server. In a final step, the local estimate…