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How can agents learn internal models that veridically represent interactions with the real world is a largely open question.
Part I: Cognitive development in children: Piaget development and learning
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Sensorimotor mismatch signals in primary visual cortex of the behaving mouse
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Smooth manifolds
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Families of group actions, generic isotriviality, and linearization
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Lie groups, Lie algebras, and representations
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Deep convolutional inverse graphics network
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Reinforcement Learning: An Introduction
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Steerable CNNs
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvarinen, A. and Morioka, H · 2016
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dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
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Independently controllable factors, 2017
Thomas, V., Pondard, J., Bengio, E., Sarfati, M., Beaudoin, P., Meurs, M.-J., Pineau, J., Precup, D., and Bengio, Y · 2017
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Group invariance principles for causal generative models
Besserve, M., Shajarisales, N., Schölkopf, B., and Janzing, D · 2018
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Neural scene representation and rendering
Eslami, S. M. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., Reichert, D. P., Buesing, L., Weber, T., Vinyals, O., Rosenbaum, D., Rabinowitz, N., King, H., Hillier, C., Botvinick, M., Wierstra, D., Kavukcuoglu, K., and Hassabis, D · 2018
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Mastering atari, go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al · 2020
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Mdp homomorphic networks: Group symmetries in reinforcement learning
van der Pol, E., Worrall, D., van Hoof, H., Oliehoek, F., and Welling, M · 2020
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A theory of independent mechanisms for extrapolation in generative models
Besserve, M., Sun, R., Janzing, D., and Schölkopf, B · 2021
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Automatic symmetry discovery with lie algebra convolutional network
Dehmamy, N., Walters, R., Liu, Y., Wang, D., and Yu, R · 2021
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A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Finzi, M., Welling, M., and Wilson, A. G · 2021
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Ha, D. and Schmidhuber, J · 2018
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Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
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On the generalization of equivariance and convolution in neural networks to the action of compact groups
Kondor, R. and Trivedi, S · 2018
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Computing the matrix exponential with an optimized taylor polynomial approximation
Bader, P., Blanes, S., and Casas, F · 2019
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Symmetry-based disentangled representation learning requires interaction with environments
Caselles-Dupré, H., Garcia-Ortiz, M., and Filliat, D · 2019
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The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep
Chaudhuri, R., Gerçek, B., Pandey, B., Peyrache, A., and Fiete, I · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucie, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2019
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Gresele, L., von Kügelgen, J., Stimper, V., Schölkopf, B., and Besserve, M · 2021
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Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y · 2021
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Causal curiosity: RL agents discovering self-supervised experiments for causal representation learning
Sontakke, S. A., Mehrjou, A., Itti, L., and Schölkopf, B · 2021
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Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
von Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., and Locatello, F · 2021
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Towards building a group-based unsupervised representation disentanglement framework, 2021
Yang, T., Ren, X., Wang, Y., Zeng, W., and Zheng, N · 2021
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Meta-learning symmetries by reparameterization
Zhou, A., Knowles, T., and Finn, C · 2021
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Learning symmetric embeddings for equivariant world models
Park, J. Y., Biza, O., Zhao, L., Van De Meent, J.-W., and Walters, R · 2022
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The evolution of brain architectures for predictive coding and active inference
Pezzulo, G., Parr, T., and Friston, K · 2022
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Quantifying and learning linear symmetry-based disentanglement
Tonnaer, L., Rey, L. A. P., Menkovski, V., Holenderski, M., and Portegies, J · 2022
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PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., Hirsh, B., Huang, S., Kalambarkar, K., Kirsch, L., Lazos, M., Lezcano, M., Liang, Y., Liang, J., Lu, Y., Luk, C., Maher, B., Pan, Y., Puhrsch, C., Reso, M., Saroufim, M., Siraichi, M. Y., Suk, H., Suo, M., Tillet, P., Wang, E., Wang, X., Wen, W., Zhang, S., Zhao, X., Zhou, K., Zou, R., Mathews, A., Chanan, G., Wu, P., and Chintala, S · 2024
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