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

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
    SSL
    DRL
ArXivPDFHTML

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

3 / 3 papers shown
Title
Exploring Causes of Representational Similarity in Machine Learning Models
Exploring Causes of Representational Similarity in Machine Learning Models
Zeyu Michael Li
Hung Anh Vu
Damilola Awofisayo
Emily Wenger
CML
20
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 relations
A. Farahat
Felix Effenberger
M. Vinck
13
5
0
12 Dec 2022
Decoupling Representation Learning from Reinforcement Learning
Decoupling Representation Learning from Reinforcement Learning
Adam Stooke
Kimin Lee
Pieter Abbeel
Michael Laskin
SSL
DRL
284
341
0
14 Sep 2020
1