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Six Lectures on Linearized Neural Networks

Six Lectures on Linearized Neural Networks

25 August 2023
Theodor Misiakiewicz
Andrea Montanari
ArXivPDFHTML

Papers citing "Six Lectures on Linearized Neural Networks"

14 / 14 papers shown
Title
ExpTest: Automating Learning Rate Searching and Tuning with Insights
  from Linearized Neural Networks
ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks
Zan Chaudhry
Naoko Mizuno
58
0
0
25 Nov 2024
Aligning Model Properties via Conformal Risk Control
Aligning Model Properties via Conformal Risk Control
William Overman
Jacqueline Jil Vallon
Mohsen Bayati
25
2
0
26 Jun 2024
Bayesian Inference for Consistent Predictions in Overparameterized
  Nonlinear Regression
Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
Tomoya Wakayama
BDL
39
0
0
06 Apr 2024
Trained quantum neural networks are Gaussian processes
Trained quantum neural networks are Gaussian processes
Filippo Girardi
Giacomo De Palma
10
3
0
13 Feb 2024
No Free Prune: Information-Theoretic Barriers to Pruning at
  Initialization
No Free Prune: Information-Theoretic Barriers to Pruning at Initialization
Tanishq Kumar
Kevin Luo
Mark Sellke
14
3
0
02 Feb 2024
Cyclic Group Projection for Enumerating Quasi-Cyclic Codes Trapping Sets
Cyclic Group Projection for Enumerating Quasi-Cyclic Codes Trapping Sets
V. Usatyuk
Yury Kuznetsov
Sergey Egorov
14
0
0
26 Jan 2024
Topology-Aware Exploration of Energy-Based Models Equilibrium: Toric
  QC-LDPC Codes and Hyperbolic MET QC-LDPC Codes
Topology-Aware Exploration of Energy-Based Models Equilibrium: Toric QC-LDPC Codes and Hyperbolic MET QC-LDPC Codes
V. Usatyuk
Denis Sapozhnikov
Sergey Egorov
9
0
0
26 Jan 2024
Learning from higher-order statistics, efficiently: hypothesis tests,
  random features, and neural networks
Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networks
Eszter Székely
Lorenzo Bardone
Federica Gerace
Sebastian Goldt
27
2
0
22 Dec 2023
SGD learning on neural networks: leap complexity and saddle-to-saddle
  dynamics
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics
Emmanuel Abbe
Enric Boix-Adserà
Theodor Misiakiewicz
FedML
MLT
76
72
0
21 Feb 2023
Learning Single-Index Models with Shallow Neural Networks
Learning Single-Index Models with Shallow Neural Networks
A. Bietti
Joan Bruna
Clayton Sanford
M. Song
160
65
0
27 Oct 2022
Neural Networks Efficiently Learn Low-Dimensional Representations with
  SGD
Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
Alireza Mousavi-Hosseini
Sejun Park
M. Girotti
Ioannis Mitliagkas
Murat A. Erdogdu
MLT
319
48
0
29 Sep 2022
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
Hong Hu
Yue M. Lu
38
15
0
13 May 2022
The Implicit Bias of Benign Overfitting
The Implicit Bias of Benign Overfitting
Ohad Shamir
91
37
0
27 Jan 2022
Learning with invariances in random features and kernel models
Learning with invariances in random features and kernel models
Song Mei
Theodor Misiakiewicz
Andrea Montanari
OOD
44
89
0
25 Feb 2021
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