ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2305.14229
  4. Cited By
Provably Learning Object-Centric Representations

Provably Learning Object-Centric Representations

23 May 2023
Jack Brady
Roland S. Zimmermann
Yash Sharma
Bernhard Schölkopf
Julius von Kügelgen
Wieland Brendel
    OCL
ArXivPDFHTML

Papers citing "Provably Learning Object-Centric Representations"

35 / 35 papers shown
Title
Compositional Risk Minimization
Compositional Risk Minimization
Divyat Mahajan
Mohammad Pezeshki
Ioannis Mitliagkas
Kartik Ahuja
Pascal Vincent
Pascal Vincent
73
4
0
08 Oct 2024
Compositional Audio Representation Learning
Compositional Audio Representation Learning
Sripathi Sridhar
Mark Cartwright
AI4TS
83
0
0
15 Sep 2024
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models
Amir Mohammad Karimi Mamaghan
Samuele Papa
Karl Henrik Johansson
Stefan Bauer
Andrea Dittadi
OCL
104
9
0
22 Jul 2024
Learning Discrete Concepts in Latent Hierarchical Models
Learning Discrete Concepts in Latent Hierarchical Models
Lingjing Kong
Guan-Hong Chen
Erdun Gao
Eric P. Xing
Yuejie Chi
Kun Zhang
79
5
0
01 Jun 2024
Bridging the Gap to Real-World Object-Centric Learning
Bridging the Gap to Real-World Object-Centric Learning
Maximilian Seitzer
Max Horn
Andrii Zadaianchuk
Dominik Zietlow
Tianjun Xiao
...
Tong He
Zheng Zhang
Bernhard Schölkopf
Thomas Brox
Francesco Locatello
OCL
91
150
0
29 Sep 2022
Function Classes for Identifiable Nonlinear Independent Component
  Analysis
Function Classes for Identifiable Nonlinear Independent Component Analysis
Simon Buchholz
M. Besserve
Bernhard Schölkopf
63
40
0
12 Aug 2022
Partial Disentanglement via Mechanism Sparsity
Partial Disentanglement via Mechanism Sparsity
Sébastien Lachapelle
Simon Lacoste-Julien
53
25
0
15 Jul 2022
SAVi++: Towards End-to-End Object-Centric Learning from Real-World
  Videos
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
Gamaleldin F. Elsayed
Aravindh Mahendran
Sjoerd van Steenkiste
Klaus Greff
Michael C. Mozer
Thomas Kipf
VOS
OCL
122
142
0
15 Jun 2022
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Yujia Zheng
Ignavier Ng
Kun Zhang
CML
36
61
0
15 Jun 2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Patrik Reizinger
Luigi Gresele
Jack Brady
Julius von Kügelgen
Dominik Zietlow
Bernhard Schölkopf
Georg Martius
Wieland Brendel
M. Besserve
DRL
59
20
0
06 Jun 2022
Causal de Finetti: On the Identification of Invariant Causal Structure
  in Exchangeable Data
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Siyuan Guo
V. Tóth
Bernhard Schölkopf
Ferenc Huszár
CML
35
37
0
29 Mar 2022
Conditional Object-Centric Learning from Video
Conditional Object-Centric Learning from Video
Thomas Kipf
Gamaleldin F. Elsayed
Aravindh Mahendran
Austin Stone
S. Sabour
G. Heigold
Rico Jonschkowski
Alexey Dosovitskiy
Klaus Greff
OCL
91
218
0
24 Nov 2021
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object
  Segmentation
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
Laurynas Karazija
Iro Laina
Christian Rupprecht
3DV
VOS
97
90
0
19 Nov 2021
Disentanglement via Mechanism Sparsity Regularization: A New Principle
  for Nonlinear ICA
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
Sébastien Lachapelle
Pau Rodríguez López
Yash Sharma
Katie Everett
Rémi Le Priol
Alexandre Lacoste
Simon Lacoste-Julien
CML
OOD
82
139
0
21 Jul 2021
Generalization and Robustness Implications in Object-Centric Learning
Generalization and Robustness Implications in Object-Centric Learning
Andrea Dittadi
Samuele Papa
Michele De Vita
Bernhard Schölkopf
Ole Winther
Francesco Locatello
OCL
OOD
62
76
0
01 Jul 2021
Independent mechanism analysis, a new concept?
Independent mechanism analysis, a new concept?
Luigi Gresele
Julius von Kügelgen
Vincent Stimper
Bernhard Schölkopf
M. Besserve
CML
45
102
0
09 Jun 2021
Self-Supervised Learning with Data Augmentations Provably Isolates
  Content from Style
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Julius von Kügelgen
Yash Sharma
Luigi Gresele
Wieland Brendel
Bernhard Schölkopf
M. Besserve
Francesco Locatello
91
314
0
08 Jun 2021
How to represent part-whole hierarchies in a neural network
How to represent part-whole hierarchies in a neural network
Geoffrey E. Hinton
OCL
MoE
71
203
0
25 Feb 2021
Towards Causal Representation Learning
Towards Causal Representation Learning
Bernhard Schölkopf
Francesco Locatello
Stefan Bauer
Nan Rosemary Ke
Nal Kalchbrenner
Anirudh Goyal
Yoshua Bengio
OOD
CML
AI4CE
109
322
0
22 Feb 2021
On the Binding Problem in Artificial Neural Networks
On the Binding Problem in Artificial Neural Networks
Klaus Greff
Sjoerd van Steenkiste
Jürgen Schmidhuber
OCL
278
263
0
09 Dec 2020
Reconstruction Bottlenecks in Object-Centric Generative Models
Reconstruction Bottlenecks in Object-Centric Generative Models
Martin Engelcke
Oiwi Parker Jones
Ingmar Posner
OCL
64
24
0
13 Jul 2020
Object-Centric Learning with Slot Attention
Object-Centric Learning with Slot Attention
Francesco Locatello
Dirk Weissenborn
Thomas Unterthiner
Aravindh Mahendran
G. Heigold
Jakob Uszkoreit
Alexey Dosovitskiy
Thomas Kipf
OCL
214
845
0
26 Jun 2020
A theory of independent mechanisms for extrapolation in generative
  models
A theory of independent mechanisms for extrapolation in generative models
M. Besserve
Rémy Sun
Dominik Janzing
Bernhard Schölkopf
58
26
0
01 Apr 2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on
  Nonlinear ICA
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA
Ilyes Khemakhem
R. Monti
Diederik P. Kingma
Aapo Hyvarinen
CML
64
114
0
26 Feb 2020
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial
  Attention and Decomposition
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Zhixuan Lin
Yi-Fu Wu
Skand Peri
Weihao Sun
Gautam Singh
Fei Deng
Jindong Jiang
Sungjin Ahn
BDL
OCL
3DPC
154
250
0
08 Jan 2020
Contrastive Learning of Structured World Models
Contrastive Learning of Structured World Models
Thomas Kipf
Elise van der Pol
Max Welling
OCL
DRL
69
284
0
27 Nov 2019
GENESIS: Generative Scene Inference and Sampling with Object-Centric
  Latent Representations
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Martin Engelcke
Adam R. Kosiorek
Oiwi Parker Jones
Ingmar Posner
OCL
112
307
0
30 Jul 2019
MONet: Unsupervised Scene Decomposition and Representation
MONet: Unsupervised Scene Decomposition and Representation
Christopher P. Burgess
Loic Matthey
Nicholas Watters
Rishabh Kabra
I. Higgins
M. Botvinick
Alexander Lerchner
OCL
84
527
0
22 Jan 2019
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
739
3,119
0
04 Jun 2018
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive
  Learning
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen
Hiroaki Sasaki
Richard Turner
OOD
CML
92
330
0
22 May 2018
Relational Neural Expectation Maximization: Unsupervised Discovery of
  Objects and their Interactions
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions
Sjoerd van Steenkiste
Michael Chang
Klaus Greff
Jürgen Schmidhuber
BDL
OCL
DRL
200
291
0
28 Feb 2018
Building machines that adapt and compute like brains
Building machines that adapt and compute like brains
Brenden M. Lake
J. Tenenbaum
AI4CE
FedML
NAI
AILaw
317
887
0
11 Nov 2017
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
  Atrous Convolution, and Fully Connected CRFs
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen
George Papandreou
Iasonas Kokkinos
Kevin Patrick Murphy
Alan Yuille
SSeg
239
18,224
0
02 Jun 2016
On Causal and Anticausal Learning
On Causal and Anticausal Learning
Bernhard Schölkopf
Dominik Janzing
J. Peters
Eleni Sgouritsa
Kun Zhang
Joris Mooij
CML
81
607
0
27 Jun 2012
Inferring deterministic causal relations
Inferring deterministic causal relations
P. Daniušis
Dominik Janzing
Joris Mooij
Jakob Zscheischler
Bastian Steudel
Kun Zhang
Bernhard Schölkopf
CML
115
192
0
15 Mar 2012
1