ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1811.07062
  4. Cited By
The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD
  Training and Sample Size

The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size

16 November 2018
Vardan Papyan
ArXivPDFHTML

Papers citing "The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size"

14 / 14 papers shown
Title
Symmetric Neural-Collapse Representations with Supervised Contrastive
  Loss: The Impact of ReLU and Batching
Symmetric Neural-Collapse Representations with Supervised Contrastive Loss: The Impact of ReLU and Batching
Ganesh Ramachandra Kini
V. Vakilian
Tina Behnia
Jaidev Gill
Christos Thrampoulidis
22
1
0
13 Jun 2023
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning
Siladittya Manna
Umapada Pal
Saumik Bhattacharya
SSL
35
1
0
24 Nov 2021
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and
  its Applications to Regularization
A Deeper Look at the Hessian Eigenspectrum of Deep Neural Networks and its Applications to Regularization
Adepu Ravi Sankar
Yash Khasbage
Rahul Vigneswaran
V. Balasubramanian
25
42
0
07 Dec 2020
Prevalence of Neural Collapse during the terminal phase of deep learning
  training
Prevalence of Neural Collapse during the terminal phase of deep learning training
Vardan Papyan
Xuemei Han
D. Donoho
35
554
0
18 Aug 2020
Exploring Weight Importance and Hessian Bias in Model Pruning
Exploring Weight Importance and Hessian Bias in Model Pruning
Mingchen Li
Yahya Sattar
Christos Thrampoulidis
Samet Oymak
30
3
0
19 Jun 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory
  Approach to Neural Network Training
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training
Diego Granziol
S. Zohren
Stephen J. Roberts
ODL
37
49
0
16 Jun 2020
Geometry of learning neural quantum states
Geometry of learning neural quantum states
Chae-Yeun Park
M. Kastoryano
32
60
0
24 Oct 2019
GradVis: Visualization and Second Order Analysis of Optimization
  Surfaces during the Training of Deep Neural Networks
GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks
Avraam Chatzimichailidis
Franz-Josef Pfreundt
N. Gauger
J. Keuper
23
10
0
26 Sep 2019
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization
Xinyan Li
Qilong Gu
Yingxue Zhou
Tiancong Chen
A. Banerjee
ODL
42
51
0
24 Jul 2019
First Exit Time Analysis of Stochastic Gradient Descent Under
  Heavy-Tailed Gradient Noise
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
T. H. Nguyen
Umut Simsekli
Mert Gurbuzbalaban
G. Richard
12
60
0
21 Jun 2019
Negative eigenvalues of the Hessian in deep neural networks
Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain
Nicolas Le Roux
Pierre-Antoine Manzagol
24
42
0
06 Feb 2019
An Investigation into Neural Net Optimization via Hessian Eigenvalue
  Density
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani
Shankar Krishnan
Ying Xiao
ODL
18
317
0
29 Jan 2019
Ambitious Data Science Can Be Painless
Ambitious Data Science Can Be Painless
Hatef Monajemi
Riccardo Murri
Eric Jonas
Percy Liang
V. Stodden
D. Donoho
26
13
0
25 Jan 2019
Measurements of Three-Level Hierarchical Structure in the Outliers in
  the Spectrum of Deepnet Hessians
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
Vardan Papyan
30
87
0
24 Jan 2019
1