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Divergent representations of ethological visual inputs emerge from
  supervised, unsupervised, and reinforcement learning
v1v2 (latest)

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning

3 December 2021
Grace W. Lindsay
J. Merel
T. Mrsic-Flogel
M. Sahani
    SSLDRL
ArXiv (abs)PDFHTML

Papers citing "Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning"

2 / 2 papers shown
Causes and Consequences of Representational Similarity in Machine Learning Models
Causes and Consequences of Representational Similarity in Machine Learning Models
Zeyu Michael Li
Hung Anh Vu
Damilola Awofisayo
Emily Wenger
CML
641
0
0
20 May 2025
A novel feature-scrambling approach reveals the capacity of
  convolutional neural networks to learn spatial relations
A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relationsNeural Networks (NN), 2022
A. Farahat
Felix Effenberger
M. Vinck
195
7
0
12 Dec 2022
1
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