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Symmetry-Based Representations for Artificial and Biological General
  Intelligence

Symmetry-Based Representations for Artificial and Biological General Intelligence

17 March 2022
I. Higgins
S. Racanière
Danilo Jimenez Rezende
    AI4CE
ArXivPDFHTML

Papers citing "Symmetry-Based Representations for Artificial and Biological General Intelligence"

50 / 81 papers shown
Title
Morphological Symmetries in Robotics
Morphological Symmetries in Robotics
Daniel Felipe Ordoñez Apraez
Giulio Turrisi
Vladimir Kostic
Mario Martin
Antonio Agudo
Francesc Moreno-Noguer
Massimiliano Pontil
Claudio Semini
Carlos Mastalli
AI4CE
65
6
0
23 Feb 2024
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred
  from Vision
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
I. Higgins
Peter Wirnsberger
Andrew Jaegle
Aleksandar Botev
68
8
0
10 Nov 2021
Self-Supervised Learning Disentangled Group Representation as Feature
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang
Zhongqi Yue
Jianqiang Huang
Qianru Sun
Hanwang Zhang
OOD
61
69
0
28 Oct 2021
Implicit Riemannian Concave Potential Maps
Implicit Riemannian Concave Potential Maps
Danilo Jimenez Rezende
S. Racanière
AI4CE
81
7
0
04 Oct 2021
Introducing Symmetries to Black Box Meta Reinforcement Learning
Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch
Sebastian Flennerhag
Hado van Hasselt
A. Friesen
Junhyuk Oh
Yutian Chen
58
32
0
22 Sep 2021
Equivariant Manifold Flows
Equivariant Manifold Flows
Isay Katsman
Aaron Lou
Derek Lim
Qingxuan Jiang
Ser-Nam Lim
Christopher De Sa
AI4CE
42
25
0
19 Jul 2021
Riemannian Convex Potential Maps
Riemannian Convex Potential Maps
Samuel N. Cohen
Brandon Amos
Y. Lipman
55
22
0
18 Jun 2021
CoAtNet: Marrying Convolution and Attention for All Data Sizes
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Zihang Dai
Hanxiao Liu
Quoc V. Le
Mingxing Tan
ViT
109
1,204
0
09 Jun 2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
350
1,148
0
27 Apr 2021
E(n) Equivariant Graph Neural Networks
E(n) Equivariant Graph Neural Networks
Victor Garcia Satorras
Emiel Hoogeboom
Max Welling
105
1,020
0
19 Feb 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon L. Batzner
Albert Musaelian
Lixin Sun
Mario Geiger
J. Mailoa
M. Kornbluth
N. Molinari
Tess E. Smidt
Boris Kozinsky
297
1,295
0
08 Jan 2021
Convex Potential Flows: Universal Probability Distributions with Optimal
  Transport and Convex Optimization
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-Wei Huang
Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
OT
168
98
0
10 Dec 2020
Representation Matters: Improving Perception and Exploration for
  Robotics
Representation Matters: Improving Perception and Exploration for Robotics
Markus Wulfmeier
Arunkumar Byravan
Tim Hertweck
I. Higgins
Ankush Gupta
...
Malcolm Reynolds
Denis Teplyashin
Roland Hafner
Thomas Lampe
Martin Riedmiller
59
15
0
03 Nov 2020
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
Elise van der Pol
Daniel E. Worrall
H. V. Hoof
F. Oliehoek
Max Welling
BDL
AI4CE
65
162
0
30 Jun 2020
Disentangling by Subspace Diffusion
Disentangling by Subspace Diffusion
David Pfau
I. Higgins
Aleksandar Botev
S. Racanière
DiffM
DRL
37
37
0
23 Jun 2020
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
F. Fuchs
Daniel E. Worrall
Volker Fischer
Max Welling
3DPC
151
692
0
18 Jun 2020
Bootstrap your own latent: A new approach to self-supervised Learning
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill
Florian Strub
Florent Altché
Corentin Tallec
Pierre Harvey Richemond
...
M. G. Azar
Bilal Piot
Koray Kavukcuoglu
Rémi Munos
Michal Valko
SSL
363
6,797
0
13 Jun 2020
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric
  Densities
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler
Leon Klein
Frank Noé
DRL
93
271
0
03 Jun 2020
Generalizing Convolutional Neural Networks for Equivariance to Lie
  Groups on Arbitrary Continuous Data
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data
Marc Finzi
Samuel Stanton
Pavel Izmailov
A. Wilson
116
323
0
25 Feb 2020
Learning Group Structure and Disentangled Representations of Dynamical
  Environments
Learning Group Structure and Disentangled Representations of Dynamical Environments
Robin Quessard
Thomas D. Barrett
W. Clements
DRL
26
21
0
17 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
358
18,752
0
13 Feb 2020
Targeted free energy estimation via learned mappings
Targeted free energy estimation via learned mappings
Peter Wirnsberger
A. J. Ballard
George Papamakarios
Stuart Abercrombie
S. Racanière
Alexander Pritzel
Danilo Jimenez Rezende
Charles Blundell
56
89
0
12 Feb 2020
Weakly-Supervised Disentanglement Without Compromises
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello
Ben Poole
Gunnar Rätsch
Bernhard Schölkopf
Olivier Bachem
Michael Tschannen
CoGe
OOD
DRL
237
317
0
07 Feb 2020
Normalizing Flows on Tori and Spheres
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende
George Papamakarios
S. Racanière
M. S. Albergo
G. Kanwar
P. Shanahan
Kyle Cranmer
TPM
84
155
0
06 Feb 2020
High-Fidelity Synthesis with Disentangled Representation
High-Fidelity Synthesis with Disentangled Representation
Wonkwang Lee
Donggyun Kim
Seunghoon Hong
Honglak Lee
DRL
58
66
0
13 Jan 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPM
AI4CE
202
1,691
0
05 Dec 2019
Disentangled Cumulants Help Successor Representations Transfer to New
  Tasks
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
Christopher Grimm
I. Higgins
André Barreto
Denis Teplyashin
Markus Wulfmeier
Tim Hertweck
R. Hadsell
Satinder Singh
59
14
0
25 Nov 2019
Equivariant Hamiltonian Flows
Equivariant Hamiltonian Flows
Danilo Jimenez Rezende
S. Racanière
I. Higgins
Peter Toth
58
64
0
30 Sep 2019
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep
  Neural Networks
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
David Pfau
J. Spencer
A. G. Matthews
W. Foulkes
75
462
0
05 Sep 2019
Explicit Disentanglement of Appearance and Perspective in Generative
  Models
Explicit Disentanglement of Appearance and Perspective in Generative Models
N. Detlefsen
Søren Hauberg
CoGe
DRL
52
49
0
11 Jun 2019
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste
Francesco Locatello
Jürgen Schmidhuber
Olivier Bachem
77
210
0
29 May 2019
Symmetry-Based Disentangled Representation Learning requires Interaction
  with Environments
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Hugo Caselles-Dupré
Michael Garcia Ortiz
David Filliat
DRL
59
66
0
30 Mar 2019
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Dominik Lorenz
Leonard Bereska
Timo Milbich
Bjorn Ommer
OCL
DRL
63
148
0
16 Mar 2019
Variational Autoencoders Pursue PCA Directions (by Accident)
Variational Autoencoders Pursue PCA Directions (by Accident)
Michal Rolínek
Dominik Zietlow
Georg Martius
OOD
DRL
67
151
0
17 Dec 2018
Counterfactuals uncover the modular structure of deep generative models
Counterfactuals uncover the modular structure of deep generative models
M. Besserve
Arash Mehrjou
Rémy Sun
Bernhard Schölkopf
DRL
BDL
DiffM
82
107
0
08 Dec 2018
Quantifying Generalization in Reinforcement Learning
Quantifying Generalization in Reinforcement Learning
K. Cobbe
Oleg Klimov
Christopher Hesse
Taehoon Kim
John Schulman
OffRL
91
670
0
06 Dec 2018
Towards a Definition of Disentangled Representations
Towards a Definition of Disentangled Representations
I. Higgins
David Amos
David Pfau
S. Racanière
Loic Matthey
Danilo Jimenez Rezende
Alexander Lerchner
OCL
DRL
95
479
0
05 Dec 2018
A Spectral Regularizer for Unsupervised Disentanglement
A Spectral Regularizer for Unsupervised Disentanglement
Aditya A. Ramesh
Youngduck Choi
Yann LeCun
DRL
68
42
0
04 Dec 2018
Challenging Common Assumptions in the Unsupervised Learning of
  Disentangled Representations
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
OOD
115
1,466
0
29 Nov 2018
Improving Generalization for Abstract Reasoning Tasks Using Disentangled
  Feature Representations
Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations
Xander Steenbrugge
Sam Leroux
Tim Verbelen
Bart Dhoedt
OOD
DRL
49
68
0
12 Nov 2018
How can deep learning advance computational modeling of sensory
  information processing?
How can deep learning advance computational modeling of sensory information processing?
J. Thompson
Yoshua Bengio
E. Formisano
M. Schönwiesner
46
16
0
25 Sep 2018
Hyperprior Induced Unsupervised Disentanglement of Latent
  Representations
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
Abdul Fatir Ansari
Harold Soh
CoGe
CML
UD
DRL
54
31
0
12 Sep 2018
Life-Long Disentangled Representation Learning with Cross-Domain Latent
  Homologies
Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
Alessandro Achille
Tom Eccles
Loic Matthey
Christopher P. Burgess
Nicholas Watters
Alexander Lerchner
I. Higgins
BDL
63
119
0
20 Aug 2018
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
Curiosity Driven Exploration of Learned Disentangled Goal Spaces
A. Laversanne-Finot
Alexandre Péré
Pierre-Yves Oudeyer
DRL
57
87
0
04 Jul 2018
Block-Value Symmetries in Probabilistic Graphical Models
Block-Value Symmetries in Probabilistic Graphical Models
Gagan Madan
Ankit Anand
Mausam
Parag Singla
31
2
0
02 Jul 2018
Rotation Equivariant CNNs for Digital Pathology
Rotation Equivariant CNNs for Digital Pathology
Bastiaan S. Veeling
J. Linmans
Jim Winkens
Taco S. Cohen
Max Welling
109
583
0
08 Jun 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CE
NAI
750
3,119
0
04 Jun 2018
Understanding disentangling in $β$-VAE
Understanding disentangling in βββ-VAE
Christopher P. Burgess
I. Higgins
Arka Pal
Loic Matthey
Nicholas Watters
Guillaume Desjardins
Alexander Lerchner
CoGe
DRL
59
829
0
10 Apr 2018
Structured Disentangled Representations
Structured Disentangled Representations
Babak Esmaeili
Hao Wu
Sarthak Jain
Alican Bozkurt
N. Siddharth
Brooks Paige
Dana H. Brooks
Jennifer Dy
Jan-Willem van de Meent
OOD
CML
BDL
DRL
75
165
0
06 Apr 2018
Learning Disentangled Joint Continuous and Discrete Representations
Learning Disentangled Joint Continuous and Discrete Representations
Emilien Dupont
DRL
78
242
0
31 Mar 2018
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