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The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
12 September 2023
Taylor W. Webb
Steven M. Frankland
Awni Altabaa
Simon N. Segert
Kamesh Krishnamurthy
Declan Campbell
Jacob Russin
Tyler Giallanza
Zack Dulberg
Randall O'Reilly
John Lafferty
Jonathan D. Cohen
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Papers citing
"The Relational Bottleneck as an Inductive Bias for Efficient Abstraction"
7 / 7 papers shown
Title
From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks
Jacob Russin
Sam Whitman McGrath
Danielle J. Williams
Lotem Elber-Dorozko
AI4CE
59
2
0
24 May 2024
Deep Neural Networks Can Learn Generalizable Same-Different Visual Relations
Alexa R. Tartaglini
Sheridan Feucht
Michael A. Lepori
Wai Keen Vong
Charles Lovering
Brenden Lake
Ellie Pavlick
ViT
OOD
14
3
0
14 Oct 2023
Meta-Learned Models of Cognition
Marcel Binz
Ishita Dasgupta
A. Jagadish
M. Botvinick
Jane X. Wang
Eric Schulz
24
23
0
12 Apr 2023
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Sébastien Bubeck
Varun Chandrasekaran
Ronen Eldan
J. Gehrke
Eric Horvitz
...
Scott M. Lundberg
Harsha Nori
Hamid Palangi
Marco Tulio Ribeiro
Yi Zhang
ELM
AI4MH
AI4CE
ALM
206
2,232
0
22 Mar 2023
Zero-shot visual reasoning through probabilistic analogical mapping
Taylor W. Webb
Shuhao Fu
Trevor J. Bihl
K. Holyoak
Hongjing Lu
LRM
34
10
0
29 Sep 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
166
1,095
0
27 Apr 2021
Emergent Symbols through Binding in External Memory
Taylor W. Webb
I. Sinha
J. Cohen
59
62
0
29 Dec 2020
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