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Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation.
Watters, N., Matthey, L., Bosnjak, M., Burgess, C. P., and Lerchner, A · 1905
Earlier work this paper cites.
Comparing partitions
Hubert, L. and Arabie, P · 1985
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An object-oriented representation for efficient reinforcement learning
Diuk, C., Cohen, A., and Littman, M. L · 2008
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One of these greebles is not like the others: Semi-supervised models for similarity structures
Stephens, R. and Navarro, D · 2008
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The different representational frameworks underpinning abstract and concrete knowledge: Evidence from odd-one-out judgements
Crutch, S. J., Connell, S., and Warrington, E. K · 2009
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Thinking, fast and slow
Kahneman, D · 2011
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
The prospects of working memory training for improving deductive reasoning
Beatty, E. L. and Vartanian, O · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P., Pascanu, R., Lai, M., Jimenez Rezende, D., et al · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
Eslami, S., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Hinton, G. E., et al · 2016
Earlier work this paper cites.
Towards deep symbolic reinforcement learning
Garnelo, M., Arulkumaran, K., and Shanahan, M · 2016
Earlier work this paper cites.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
Kansky, K., Silver, T., Mély, D. A., Eldawy, M., Lázaro-Gredilla, M., Lou, X., Dorfman, N., Sidor, S., Phoenix, S., and George, D · 2017
Earlier work this paper cites.
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017
Earlier work this paper cites.
A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Visual interaction networks: Learning a physics simulator from video
Watters, N., Zoran, D., Weber, T., Battaglia, P., Pascanu, R., and Tacchetti, A · 2017
Earlier work this paper cites.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 2018
Earlier work this paper cites.
Sequential attend, infer, repeat: Generative modelling of moving objects
Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
Earlier work this paper cites.
Deep reinforcement learning with relational inductive biases
Zambaldi, V., Raposo, D., Santoro, A., Bapst, V., Li, Y., Babuschkin, I., Tuyls, K., Reichert, D., Lillicrap, T., Lockhart, E., et al · 2018
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Monet: Unsupervised scene decomposition and representation
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019
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Spatially invariant unsupervised object detection with convolutional neural networks
Crawford, E. and Pineau, J · 2019
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Genesis: Generative scene inference and sampling with object-centric latent representations
Engelcke, M., Kosiorek, A. R., Jones, O. P., and Posner, I · 2019
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Recurrent independent mechanisms
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B · 2019
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Wüthrich, M., Bengio, Y., Schölkopf, B., and Bauer, S · 2021
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Roots: Object-centric representation and rendering of 3d scenes
Chen, C., Deng, F., and Ahn, S · 2021
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Generalization and robustness implications in object-centric learning
Dittadi, A., Papa, S., De Vita, M., Schölkopf, B., Winther, O., and Locatello, F · 2021
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Genesis-v2: Inferring unordered object representations without iterative refinement
Engelcke, M., Parker Jones, O., and Posner, I · 2021
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Systematic evaluation of causal discovery in visual model based reinforcement learning
Ke, N. R., Didolkar, A., Mittal, S., Goyal, A., Lajoie, G., Bauer, S., Rezende, D., Bengio, Y., Mozer, M., and Pal, C · 2021
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Multi-object representation learning with iterative variational inference
Greff, K., Kaufman, R. L., Kabra, R., Watters, N., Burgess, C., Zoran, D., Matthey, L., Botvinick, M., and Lerchner, A · 2019
Cited alongside, same era.
Dream to control: Learning behaviors by latent imagination
Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 2019
Cited alongside, same era.
Scalor: Generative world models with scalable object representations
Jiang, J., Janghorbani, S., De Melo, G., and Ahn, S · 2019
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Contrastive learning of structured world models
Kipf, T., van der Pol, E., and Welling, M · 2019
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Space: Unsupervised object-oriented scene representation via spatial attention and decomposition
Lin, Z., Wu, Y.-F., Peri, S. V., Sun, W., Singh, G., Deng, F., Jiang, J., and Ahn, S · 2019
Cited alongside, same era.
Stable baselines3, 2019
Raffin, A., Hill, A., Ernestus, M., Gleave, A., Kanervisto, A., and Dormann, N · 2019
Cited alongside, same era.
A perspective on objects and systematic generalization in model-based rl
van Steenkiste, S., Greff, K., and Schmidhuber, J · 2019
Cited alongside, same era.
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Conditional object-centric learning from video
Kipf, T., Elsayed, G. F., Mahendran, A., Stone, A., Sabour, S., Heigold, G., Jonschkowski, R., Dosovitskiy, A., and Greff, K · 2021
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The role of pretrained representations for the ood generalization of rl agents
Träuble, F., Dittadi, A., Wuthrich, M., Widmaier, F., Gehler, P. V., Winther, O., Locatello, F., Bachem, O., Schölkopf, B., and Bauer, S · 2021
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Generative video transformer: Can objects be the words?
Wu, Y.-F., Yoon, J., and Ahn, S · 2021
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Savi++: Towards end-to-end object-centric learning from real-world videos
Elsayed, G. F., Mahendran, A., van Steenkiste, S., Greff, K., Mozer, M. C., and Kipf, T · 2022
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Visuomotor control in multi-object scenes using object-aware representations
Heravi, N., Wahid, A., Lynch, C., Florence, P., Armstrong, T., Tompson, J., Sermanet, P., Bohg, J., and Dwibedi, D · 2022
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Tell me why! explanations support learning relational and causal structure
Lampinen, A. K., Roy, N., Dasgupta, I., Chan, S. C., Tam, A., Mcclelland, J., Yan, C., Santoro, A., Rabinowitz, N. C., Wang, J., et al · 2022
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Compositional multi-object reinforcement learning with linear relation networks
Mambelli, D., Träuble, F., Bauer, S., Schölkopf, B., and Locatello, F · 2022
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Simple unsupervised object-centric learning for complex and naturalistic videos
Singh, G., Wu, Y.-F., and Ahn, S · 2022
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An investigation into the open world survival game crafter
Stanić, A., Tang, Y., Ha, D., and Schmidhuber, J · 2022
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Masked visual pre-training for motor control
Xiao, T., Radosavovic, I., Darrell, T., and Malik, J · 2022
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Policy architectures for compositional generalization in control
Zhou, A., Kumar, V., Finn, C., and Rajeswaran, A · 2022
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Jiang, J., Deng, F., Singh, G., and Ahn, S · 2023
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Neural systematic binder
Singh, G., Kim, Y., and Ahn, S · 2023
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