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A Law of Data Separation in Deep Learning

A Law of Data Separation in Deep Learning

31 October 2022
Hangfeng He
Weijie J. Su
    OOD
ArXivPDFHTML

Papers citing "A Law of Data Separation in Deep Learning"

29 / 29 papers shown
Title
Understanding How Nonlinear Layers Create Linearly Separable Features for Low-Dimensional Data
Alec S. Xu
Can Yaras
Peng Wang
Q. Qu
23
0
0
04 Jan 2025
The Persistence of Neural Collapse Despite Low-Rank Bias: An Analytic
  Perspective Through Unconstrained Features
The Persistence of Neural Collapse Despite Low-Rank Bias: An Analytic Perspective Through Unconstrained Features
Connall Garrod
Jonathan P. Keating
34
1
0
30 Oct 2024
Collapsed Language Models Promote Fairness
Collapsed Language Models Promote Fairness
Jingxuan Xu
Wuyang Chen
Linyi Li
Yao Zhao
Yunchao Wei
39
0
0
06 Oct 2024
The Prevalence of Neural Collapse in Neural Multivariate Regression
The Prevalence of Neural Collapse in Neural Multivariate Regression
G. Andriopoulos
Zixuan Dong
Li Guo
Zifan Zhao
Keith Ross
28
2
0
06 Sep 2024
A Law of Next-Token Prediction in Large Language Models
A Law of Next-Token Prediction in Large Language Models
Hangfeng He
Weijie J. Su
24
5
0
24 Aug 2024
A spring-block theory of feature learning in deep neural networks
A spring-block theory of feature learning in deep neural networks
Chengzhi Shi
Liming Pan
Ivan Dokmanić
AI4CE
28
1
0
28 Jul 2024
Towards Interpretable Deep Local Learning with Successive Gradient
  Reconciliation
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Yibo Yang
Xiaojie Li
Motasem Alfarra
Hasan Hammoud
Adel Bibi
Philip H. S. Torr
Bernard Ghanem
30
2
0
07 Jun 2024
Compressible Dynamics in Deep Overparameterized Low-Rank Learning &
  Adaptation
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Can Yaras
Peng Wang
Laura Balzano
Qing Qu
AI4CE
27
12
0
06 Jun 2024
Linguistic Collapse: Neural Collapse in (Large) Language Models
Linguistic Collapse: Neural Collapse in (Large) Language Models
Robert Wu
V. Papyan
33
11
0
28 May 2024
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really
  Optimal?
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
Peter Súkeník
Marco Mondelli
Christoph H. Lampert
AI4CE
30
5
0
23 May 2024
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD
  with Near-perfect Representation Learning
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning
Chendi Wang
Yuqing Zhu
Weijie J. Su
Yu-Xiang Wang
AAML
35
4
0
14 May 2024
Progressive Feedforward Collapse of ResNet Training
Progressive Feedforward Collapse of ResNet Training
Sicong Wang
Kuo Gai
Shihua Zhang
AI4CE
25
4
0
02 May 2024
Unifying Low Dimensional Observations in Deep Learning Through the Deep
  Linear Unconstrained Feature Model
Unifying Low Dimensional Observations in Deep Learning Through the Deep Linear Unconstrained Feature Model
Connall Garrod
Jonathan P. Keating
18
8
0
09 Apr 2024
Average gradient outer product as a mechanism for deep neural collapse
Average gradient outer product as a mechanism for deep neural collapse
Daniel Beaglehole
Peter Súkeník
Marco Mondelli
Misha Belkin
AI4CE
25
12
0
21 Feb 2024
On the Robustness of Neural Collapse and the Neural Collapse of
  Robustness
On the Robustness of Neural Collapse and the Neural Collapse of Robustness
Jingtong Su
Ya Shi Zhang
Nikolaos Tsilivis
Julia Kempe
AAML
15
4
0
13 Nov 2023
Detecting Out-of-Distribution Through the Lens of Neural Collapse
Detecting Out-of-Distribution Through the Lens of Neural Collapse
Litian Liu
Yao Qin
OODD
23
5
0
02 Nov 2023
Neural Collapse in Multi-label Learning with Pick-all-label Loss
Neural Collapse in Multi-label Learning with Pick-all-label Loss
Pengyu Li
Xiao Li
Yutong Wang
Qing Qu
8
6
0
24 Oct 2023
A Neural Collapse Perspective on Feature Evolution in Graph Neural
  Networks
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
Vignesh Kothapalli
Tom Tirer
Joan Bruna
22
10
0
04 Jul 2023
Quantifying the Variability Collapse of Neural Networks
Quantifying the Variability Collapse of Neural Networks
Jing-Xue Xu
Haoxiong Liu
23
4
0
06 Jun 2023
The Law of Parsimony in Gradient Descent for Learning Deep Linear
  Networks
The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
Can Yaras
P. Wang
Wei Hu
Zhihui Zhu
Laura Balzano
Qing Qu
20
17
0
01 Jun 2023
White-Box Transformers via Sparse Rate Reduction
White-Box Transformers via Sparse Rate Reduction
Yaodong Yu
Sam Buchanan
Druv Pai
Tianzhe Chu
Ziyang Wu
Shengbang Tong
B. Haeffele
Y. Ma
ViT
16
80
0
01 Jun 2023
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained
  Features Model
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
Peter Súkeník
Marco Mondelli
Christoph H. Lampert
13
20
0
22 May 2023
Principled and Efficient Transfer Learning of Deep Models via Neural
  Collapse
Principled and Efficient Transfer Learning of Deep Models via Neural Collapse
Xiao Li
Sheng Liu
Jin-li Zhou
Xin Lu
C. Fernandez‐Granda
Zhihui Zhu
Q. Qu
AAML
16
18
0
23 Dec 2022
Perturbation Analysis of Neural Collapse
Perturbation Analysis of Neural Collapse
Tom Tirer
Haoxiang Huang
Jonathan Niles-Weed
AAML
27
23
0
29 Oct 2022
Neural Networks as Paths through the Space of Representations
Neural Networks as Paths through the Space of Representations
Richard D. Lange
Devin Kwok
Jordan K Matelsky
Xinyue Wang
David Rolnick
Konrad Paul Kording
19
4
0
22 Jun 2022
Nearest Class-Center Simplification through Intermediate Layers
Nearest Class-Center Simplification through Intermediate Layers
Ido Ben-Shaul
S. Dekel
32
26
0
21 Jan 2022
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse
  in Imbalanced Training
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
Cong Fang
Hangfeng He
Qi Long
Weijie J. Su
FAtt
114
162
0
29 Jan 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,214
0
17 Apr 2017
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
L. V. D. van der Maaten
Kilian Q. Weinberger
PINN
3DV
244
35,884
0
25 Aug 2016
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