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Memorization and Optimization in Deep Neural Networks with Minimum
  Over-parameterization

Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization

20 May 2022
Simone Bombari
Mohammad Hossein Amani
Marco Mondelli
ArXivPDFHTML

Papers citing "Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization"

5 / 5 papers shown
Title
Reparameterization invariance in approximate Bayesian inference
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy
M. Miani
Carl Henrik Ek
Philipp Hennig
Marvin Pfortner
Lukas Tatzel
Søren Hauberg
BDL
42
8
0
05 Jun 2024
Memorization Capacity of Multi-Head Attention in Transformers
Memorization Capacity of Multi-Head Attention in Transformers
Sadegh Mahdavi
Renjie Liao
Christos Thrampoulidis
24
22
0
03 Jun 2023
Beyond the Universal Law of Robustness: Sharper Laws for Random Features
  and Neural Tangent Kernels
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
Simone Bombari
Shayan Kiyani
Marco Mondelli
AAML
18
10
0
03 Feb 2023
On the Proof of Global Convergence of Gradient Descent for Deep ReLU
  Networks with Linear Widths
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths
Quynh N. Nguyen
33
49
0
24 Jan 2021
Deep Networks and the Multiple Manifold Problem
Deep Networks and the Multiple Manifold Problem
Sam Buchanan
D. Gilboa
John N. Wright
166
39
0
25 Aug 2020
1