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Generalizing vision-based reinforcement learning (RL) agents to novel environments remains a difficult and open challenge.
Neural networks and physical systems with emergent collective computational abilities
Hopfield, J. J · 1982
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Learning to predict by the methods of temporal differences
Sutton, R. S · 1988
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Microstructure of a spatial map in the entorhinal cortex
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., and Moser, E. I · 2005
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Representation of geometric borders in the entorhinal cortex
Solstad, T., Boccara, C. N., Kropff, E., Moser, M.-B., and Moser, E. I · 2008
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The free-energy principle: a unified brain theory?
Friston, K · 2010
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How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., and Rust, N. C · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A. C · 2013
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The role of the hippocampus in flexible cognition and social behavior
Rubin, R. D., Watson, P. D., Duff, M. C., and Cohen, N. J · 2014
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Dense associative memory for pattern recognition
Krotov, D. and Hopfield, J. J · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C. P., Glorot, X., Botvinick, M. M., Mohamed, S., and Lerchner, A · 2017
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DARLA: improving zero-shot transfer in reinforcement learning
Higgins, I., Pal, A., Rusu, A. A., Matthey, L., Burgess, C. P., Pritzel, A., Botvinick, M. M., Blundell, C., and Lerchner, A · 2017
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
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The 2017 davis challenge on video object segmentation
Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., and Van Gool, L · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
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Attention is all you need
Vaswani, A · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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What is a cognitive map? organizing knowledge for flexible behavior
Behrens, T. E., Muller, T. H., Whittington, J. C., Mark, S., Baram, A. B., Stachenfeld, K. L., and Kurth-Nelson, Z · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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SCAN: learning hierarchical compositional visual concepts
Higgins, I., Sonnerat, N., Matthey, L., Pal, A., Burgess, C. P., Bosnjak, M., Shanahan, M., Botvinick, M. M., Hassabis, D., and Lerchner, A · 2018
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Sax, A., Emi, B., Zamir, A. R., Guibas, L., Savarese, S., and Malik, J · 2018
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Universal hopfield networks: A general framework for single-shot associative memory models
Millidge, B., Salvatori, T., Song, Y., Lukasiewicz, T., and Bogacz, R · 2022
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Visual representation learning does not generalize strongly within the same domain
Schott, L., von Kügelgen, J., Träuble, F., Gehler, P. V., Russell, C., Bethge, M., Schölkopf, B., Locatello, F., and Brendel, W · 2022
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Task-induced representation learning
Yamada, J., Pertsch, K., Gunjal, A., and Lim, J. J · 2022
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Birth of a transformer: A memory viewpoint
Bietti, A., Cabannes, V., Bouchacourt, D., Jégou, H., and Bottou, L · 2023
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Temporal disentanglement of representations for improved generalisation in reinforcement learning
Dunion, M., McInroe, T., Luck, K. S., Hanna, J. P., and Albrecht, S. V · 2023
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., de Las Casas, D., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., Lillicrap, T. P., and Riedmiller, M. A · 2018
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Object-vector coding in the medial entorhinal cortex
Høydal, Ø. A., Skytøen, E. R., Andersson, S. O., Moser, M.-B., and Moser, E. I · 2019
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Hopfield networks is all you need
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Disentanglement via latent quantization
Hsu, K., Dorrell, W., Whittington, J. C. R., Wu, J., and Finn, C · 2023
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Disentanglement with biological constraints: A theory of functional cell types
Whittington, J. C. R., Dorrell, W., Ganguli, S., and Behrens, T · 2023
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Repo: Resilient model-based reinforcement learning by regularizing posterior predictability
Zhu, C., Simchowitz, M., Gadipudi, S., and Gupta, A · 2023
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A recipe for unbounded data augmentation in visual reinforcement learning
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