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On 1/n neural representation and robustness

On 1/n neural representation and robustness

8 December 2020
Josue Nassar
Piotr A. Sokól
SueYeon Chung
K. Harris
Il Memming Park
    AAMLOOD
ArXiv (abs)PDFHTML

Papers citing "On 1/n neural representation and robustness"

13 / 13 papers shown
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
Zihang Liu
Tianyu Pang
Oleg Balabanov
Chaoqun Yang
Tianjin Huang
L. Yin
Yaoqing Yang
Shiwei Liu
LRM
310
7
0
01 Jun 2025
Model Balancing Helps Low-data Training and Fine-tuning
Model Balancing Helps Low-data Training and Fine-tuningConference on Empirical Methods in Natural Language Processing (EMNLP), 2024
Zihang Liu
Yihan Hu
Tianyu Pang
Yefan Zhou
Pu Ren
Yaoqing Yang
253
10
0
16 Oct 2024
Limited but consistent gains in adversarial robustness by co-training
  object recognition models with human EEG
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG
Manshan Guo
Bhavin Choksi
Sari Saba-Sadiya
A. T. Gifford
Martina G. Vilas
Radoslaw Martin Cichy
Gemma Roig
AAML
255
2
0
05 Sep 2024
Discretization of continuous input spaces in the hippocampal autoencoder
Discretization of continuous input spaces in the hippocampal autoencoder
A. F. Amil
Ismael T. Freire
P. Verschure
158
6
0
23 May 2024
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network
  Training
Temperature Balancing, Layer-wise Weight Analysis, and Neural Network TrainingNeural Information Processing Systems (NeurIPS), 2023
Yefan Zhou
Tianyu Pang
Keqin Liu
Charles H. Martin
Michael W. Mahoney
Yaoqing Yang
460
18
0
01 Dec 2023
A Spectral Theory of Neural Prediction and Alignment
A Spectral Theory of Neural Prediction and AlignmentNeural Information Processing Systems (NeurIPS), 2023
Abdulkadir Canatar
J. Feather
Albert J. Wakhloo
SueYeon Chung
OOD
298
20
0
22 Sep 2023
Learning Efficient Coding of Natural Images with Maximum Manifold
  Capacity Representations
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
T. Yerxa
Yilun Kuang
Eero P. Simoncelli
SueYeon Chung
320
0
0
06 Mar 2023
Isometric Representations in Neural Networks Improve Robustness
Isometric Representations in Neural Networks Improve RobustnessScientific Reports (Sci Rep), 2022
Kosio Beshkov
Jonas Verhellen
M. Lepperød
AAMLOOD
172
1
0
02 Nov 2022
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural
  Data Regularizer
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer
Cassidy Pirlot
Richard C. Gerum
Cory Efird
J. Zylberberg
Alona Fyshe
OOD
221
6
0
06 Sep 2022
Different Spectral Representations in Optimized Artificial Neural
  Networks and Brains
Different Spectral Representations in Optimized Artificial Neural Networks and Brains
Richard C. Gerum
Cassidy Pirlot
Alona Fyshe
J. Zylberberg
AAML
173
1
0
22 Aug 2022
The Devil is in the Frequency: Geminated Gestalt Autoencoder for
  Self-Supervised Visual Pre-Training
The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-TrainingAAAI Conference on Artificial Intelligence (AAAI), 2022
Hao Liu
Xinghua Jiang
Xin Li
Antai Guo
Deqiang Jiang
Bo Ren
219
45
0
18 Apr 2022
Investigating Power laws in Deep Representation Learning
Investigating Power laws in Deep Representation Learning
Arna Ghosh
Arnab Kumar Mondal
Kumar Krishna Agrawal
Blake A. Richards
SSLOOD
200
21
0
11 Feb 2022
Optimal input representation in neural systems at the edge of chaos
Optimal input representation in neural systems at the edge of chaos
Guillermo B. Morales
M. A. Muñoz
AAML
228
22
0
12 Jul 2021
1
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