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2402.10202
Cited By
Bridging Associative Memory and Probabilistic Modeling
15 February 2024
Rylan Schaeffer
Nika Zahedi
Mikail Khona
Dhruv Pai
Sang T. Truong
Yilun Du
Mitchell Ostrow
Sarthak Chandra
Andres Carranza
Ila Rani Fiete
Andrey Gromov
Oluwasanmi Koyejo
DiffM
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Papers citing
"Bridging Associative Memory and Probabilistic Modeling"
18 / 18 papers shown
Title
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman
Peter J. Liu
Lechao Xiao
Katie Everett
A. Alemi
...
Jascha Narain Sohl-Dickstein
Kelvin Xu
Jaehoon Lee
Justin Gilmer
Simon Kornblith
68
98
0
25 Sep 2023
The emergence of clusters in self-attention dynamics
Borjan Geshkovski
Cyril Letrouit
Yury Polyanskiy
Philippe Rigollet
53
52
0
09 May 2023
What Can Transformers Learn In-Context? A Case Study of Simple Function Classes
Shivam Garg
Dimitris Tsipras
Percy Liang
Gregory Valiant
116
504
0
01 Aug 2022
Streaming Inference for Infinite Non-Stationary Clustering
Rylan Schaeffer
Gabrielle K. Liu
Yilun Du
Scott W. Linderman
Ila Rani Fiete
48
1
0
02 May 2022
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
Stephanie C. Y. Chan
Adam Santoro
Andrew Kyle Lampinen
Jane X. Wang
Aaditya K. Singh
Pierre Harvey Richemond
J. Mcclelland
Felix Hill
116
261
0
22 Apr 2022
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge
Tommaso Salvatori
Yuhang Song
Thomas Lukasiewicz
Rafal Bogacz
VLM
42
54
0
09 Feb 2022
Content Addressable Memory Without Catastrophic Forgetting by Heteroassociation with a Fixed Scaffold
Sugandha Sharma
Sarthak Chandra
Ila R. Fiete
94
22
0
01 Feb 2022
Attention Approximates Sparse Distributed Memory
Trenton Bricken
Cengiz Pehlevan
62
34
0
10 Nov 2021
Unsupervised Learning of Compositional Energy Concepts
Yilun Du
Shuang Li
Yash Sharma
J. Tenenbaum
Igor Mordatch
CoGe
OCL
59
80
0
04 Nov 2021
Large Associative Memory Problem in Neurobiology and Machine Learning
Dmitry Krotov
J. Hopfield
46
136
0
16 Aug 2020
Hopfield Networks is All You Need
Hubert Ramsauer
Bernhard Schafl
Johannes Lehner
Philipp Seidl
Michael Widrich
...
David P. Kreil
Michael K Kopp
Günter Klambauer
Johannes Brandstetter
Sepp Hochreiter
83
429
0
16 Jul 2020
On Layer Normalization in the Transformer Architecture
Ruibin Xiong
Yunchang Yang
Di He
Kai Zheng
Shuxin Zheng
Chen Xing
Huishuai Zhang
Yanyan Lan
Liwei Wang
Tie-Yan Liu
AI4CE
112
988
0
12 Feb 2020
Learning Deep Transformer Models for Machine Translation
Qiang Wang
Bei Li
Tong Xiao
Jingbo Zhu
Changliang Li
Derek F. Wong
Lidia S. Chao
70
670
0
05 Jun 2019
Generating Long Sequences with Sparse Transformers
R. Child
Scott Gray
Alec Radford
Ilya Sutskever
93
1,894
0
23 Apr 2019
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models
Erik Nijkamp
Mitch Hill
Tian Han
Song-Chun Zhu
Ying Nian Wu
51
154
0
29 Mar 2019
Adaptive Input Representations for Neural Language Modeling
Alexei Baevski
Michael Auli
96
390
0
28 Sep 2018
Layer Normalization
Jimmy Lei Ba
J. Kiros
Geoffrey E. Hinton
336
10,467
0
21 Jul 2016
Revisiting k-means: New Algorithms via Bayesian Nonparametrics
Brian Kulis
Michael I. Jordan
71
390
0
02 Nov 2011
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