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On the Power and Limitations of Random Features for Understanding Neural Networks

1 April 2019

Papers citing "On the Power and Limitations of Random Features for Understanding Neural Networks"

50 / 91 papers shown

Title
Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation Ninh Pham Rasmus Pagh 138 0 0 13 May 2025
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input Ziang Chen Rong Ge MLT 154 1 0 10 Jan 2025
Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression Aku Kammonen Anamika Pandey E. von Schwerin Raúl Tempone 76 0 0 08 Oct 2024
Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control Andrew G. Lamperski Tyler Lekang 53 3 0 25 Mar 2024
Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets A. D. Cunha Francesco d’Amore Emanuele Natale MLT 66 1 0 16 Nov 2023
Orthogonal Random Features: Explicit Forms and Sharp Inequalities N. Demni Hachem Kadri 74 1 0 11 Oct 2023
Six Lectures on Linearized Neural Networks Theodor Misiakiewicz Andrea Montanari 143 13 0 25 Aug 2023
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks Eshaan Nichani Alexandru Damian Jason D. Lee MLT 201 15 0 11 May 2023
Depth Separation with Multilayer Mean-Field Networks Y. Ren Mo Zhou Rong Ge OOD 85 3 0 03 Apr 2023
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control Tyler Lekang Andrew G. Lamperski 54 0 0 28 Mar 2023
Online Learning for the Random Feature Model in the Student-Teacher Framework Roman Worschech B. Rosenow 86 0 0 24 Mar 2023
Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron Weihang Xu S. Du 108 16 0 20 Feb 2023
System identification of neural systems: If we got it right, would we know? Yena Han T. Poggio Brian Cheung 94 10 0 13 Feb 2023
On the symmetries in the dynamics of wide two-layer neural networks Karl Hajjar Lénaïc Chizat 51 11 0 16 Nov 2022
Understanding Impacts of Task Similarity on Backdoor Attack and Detection Di Tang Rui Zhu Wenyuan Xu Haixu Tang Yi Chen AAML 118 5 0 12 Oct 2022
Annihilation of Spurious Minima in Two-Layer ReLU Networks Yossi Arjevani M. Field 52 8 0 12 Oct 2022
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD Alireza Mousavi-Hosseini Sejun Park M. Girotti Ioannis Mitliagkas Murat A. Erdogdu MLT 379 50 0 29 Sep 2022
Understanding Deep Neural Function Approximation in Reinforcement Learning via $ε$ -Greedy Exploration Fanghui Liu Luca Viano Volkan Cevher 116 20 0 15 Sep 2022
Differentiable Architecture Search with Random Features Xuanyang Zhang Yonggang Li Xinming Zhang Yongtao Wang Jian Sun 70 11 0 18 Aug 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Boaz Barak Benjamin L. Edelman Surbhi Goel Sham Kakade Eran Malach Cyril Zhang 114 133 0 18 Jul 2022
Learning sparse features can lead to overfitting in neural networks Leonardo Petrini Francesco Cagnetta Eric Vanden-Eijnden Matthieu Wyart MLT 103 26 0 24 Jun 2022
$Intrinsic dimensionality and generalization properties of the $\mathcal{R}$-norm inductive bias$ Intrinsic dimensionality and generalization properties of the $\mathcal{R}$ -norm inductive bias Navid Ardeshir Daniel J. Hsu Clayton Sanford CML AI4CE 113 6 0 10 Jun 2022
Long-Tailed Learning Requires Feature Learning T. Laurent J. V. Brecht Xavier Bresson VLM 93 1 0 29 May 2022
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable Promit Ghosal Srinath Mahankali Yihang Sun MLT 64 5 0 24 May 2022
Learning a Single Neuron for Non-monotonic Activation Functions Lei Wu MLT 65 11 0 16 Feb 2022
Random Feature Amplification: Feature Learning and Generalization in Neural Networks Spencer Frei Niladri S. Chatterji Peter L. Bartlett MLT 103 30 0 15 Feb 2022
Optimization-Based Separations for Neural Networks Itay Safran Jason D. Lee 387 14 0 04 Dec 2021
Subquadratic Overparameterization for Shallow Neural Networks Chaehwan Song Ali Ramezani-Kebrya Thomas Pethick Armin Eftekhari Volkan Cevher 81 31 0 02 Nov 2021
Provable Regret Bounds for Deep Online Learning and Control Xinyi Chen Edgar Minasyan Jason D. Lee Elad Hazan 115 6 0 15 Oct 2021
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks Shuai Zhang Meng Wang Sijia Liu Pin-Yu Chen Jinjun Xiong UQCV MLT 85 13 0 12 Oct 2021
ReLU Regression with Massart Noise Ilias Diakonikolas Jongho Park Christos Tzamos 109 12 0 10 Sep 2021
A spectral-based analysis of the separation between two-layer neural networks and linear methods Lei Wu Jihao Long 126 8 0 10 Aug 2021
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe Pritish Kamath Eran Malach Colin Sandon Nathan Srebro MLT 125 23 0 09 Aug 2021
Deep Networks Provably Classify Data on Curves Tingran Wang Sam Buchanan D. Gilboa John N. Wright 83 9 0 29 Jul 2021
Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II Yossi Arjevani M. Field 70 19 0 21 Jul 2021
Going Beyond Linear RL: Sample Efficient Neural Function Approximation Baihe Huang Kaixuan Huang Sham Kakade Jason D. Lee Qi Lei Runzhe Wang Jiaqi Yang 103 8 0 14 Jul 2021
Memory-efficient Transformers via Top- $k$ Attention Ankit Gupta Guy Dar Shaya Goodman David Ciprut Jonathan Berant MQ 98 60 0 13 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 76 27 0 11 Jun 2021
Neural Optimization Kernel: Towards Robust Deep Learning Yueming Lyu Ivor Tsang 58 1 0 11 Jun 2021
Learning a Single Neuron with Bias Using Gradient Descent Gal Vardi Gilad Yehudai Ohad Shamir MLT 89 17 0 02 Jun 2021
Properties of the After Kernel Philip M. Long 66 29 0 21 May 2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth Keyulu Xu Mozhi Zhang Stefanie Jegelka Kenji Kawaguchi GNN 53 78 0 10 May 2021
Relative stability toward diffeomorphisms indicates performance in deep nets Leonardo Petrini Alessandro Favero Mario Geiger Matthieu Wyart OOD 93 15 0 06 May 2021
Noether: The More Things Change, the More Stay the Same Grzegorz Gluch R. Urbanke 79 18 0 12 Apr 2021
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks Yuval Belfer Amnon Geifman Meirav Galun Ronen Basri 74 17 0 07 Apr 2021
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels Eran Malach Pritish Kamath Emmanuel Abbe Nathan Srebro 88 39 0 01 Mar 2021
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed Maria Refinetti Sebastian Goldt Florent Krzakala Lenka Zdeborová 92 74 0 23 Feb 2021
On the Approximation Power of Two-Layer Networks of Random ReLUs Daniel J. Hsu Clayton Sanford Rocco A. Servedio Emmanouil-Vasileios Vlatakis-Gkaragkounis 63 25 0 03 Feb 2021
The Connection Between Approximation, Depth Separation and Learnability in Neural Networks Eran Malach Gilad Yehudai Shai Shalev-Shwartz Ohad Shamir 95 20 0 31 Jan 2021
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis Atsushi Nitanda Denny Wu Taiji Suzuki 97 29 0 31 Dec 2020