Diving into the shallows: a computational perspective on large-scale shallow learning

30 March 2017

Papers citing "Diving into the shallows: a computational perspective on large-scale shallow learning"

15 / 15 papers shown

Title
Many Perception Tasks are Highly Redundant Functions of their Input Data Rahul Ramesh Anthony Bisulco Ronald W. DiTullio Linran Wei Vijay Balasubramanian Kostas Daniilidis Pratik Chaudhari 44 2 0 18 Jul 2024
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning Michal Dereziñski Christopher Musco Jiaming Yang 48 2 0 09 May 2024
Changing the Kernel During Training Leads to Double Descent in Kernel Regression Oskar Allerbo 38 0 0 03 Nov 2023
A Simple Algorithm For Scaling Up Kernel Methods Tengyu Xu Bryan Kelly Semyon Malamud 21 0 0 26 Jan 2023
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features Mateus P. Otto Rafael Izbicki 37 1 0 11 Nov 2022
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting Neil Rohit Mallinar James B. Simon Amirhesam Abedsoltan Parthe Pandit M. Belkin Preetum Nakkiran 26 37 0 14 Jul 2022
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks P. Esser L. C. Vankadara D. Ghoshdastidar 28 53 0 07 Dec 2021
Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks Adityanarayanan Radhakrishnan George Stefanakis M. Belkin Caroline Uhler 30 25 0 31 Jul 2021
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 22 109 0 10 Aug 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 30 113 0 18 Jun 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 41 214 0 03 Dec 2019
On the Spectral Bias of Neural Networks Nasim Rahaman A. Baratin Devansh Arpit Felix Dräxler Min Lin Fred Hamprecht Yoshua Bengio Aaron Courville 57 1,395 0 22 Jun 2018
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models Hugh Salimbeni Stefanos Eleftheriadis J. Hensman BDL 25 85 0 24 Mar 2018
Approximation beats concentration? An approximation view on inference with smooth radial kernels M. Belkin 34 69 0 10 Jan 2018
The Marginal Value of Adaptive Gradient Methods in Machine Learning Ashia Wilson Rebecca Roelofs Mitchell Stern Nathan Srebro Benjamin Recht ODL 20 1,013 0 23 May 2017