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Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer.
A meta-transfer objective for learning to disentangle causal mechanisms
Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C. (2019) · 1901
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Multi-object representation learning with iterative variational inference
Greff, K., Kaufmann, R. L., Kabra, R., Watters, N., Burgess, C., Zoran, D., Matthey, L., Botvinick, M., and Lerchner, A. (2019) · 1903
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Recurrent independent mechanisms
Goyal, A., Lamb, A., Hoffmann, J., Sodhani, S., Levine, S., Bengio, Y., and Schölkopf, B. (2019) · 1909
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G. (2009) · 2009
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Deep learning of representations: Looking forward
Bengio, Y. (2013) · 2013
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Neural module networks
Andreas, J., Rohrbach, M., Darrell, T., and Klein, D. (2016) · 2016
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Improved techniques for training gans. arxiv 2016
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Strategic attentive writer for learning macro-actions
Vezhnevets, A., Mnih, V., Osindero, S., Graves, A., Vinyals, O., Agapiou, J., et al. (2016) · 2016
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W. (2017) · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A. A., Pritzel, A., and Wierstra, D. (2017) · 2017
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Dynamic representation of partially occluded objects in primate prefrontal and visual cortex
Fyall, A. M., El-Shamayleh, Y., Choi, H., Shea-Brown, E., and Pasupathy, A. (2017) · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017) · 2017
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Residual connections encourage iterative inference
Jastrzębski, S., Arpit, D., Ballas, N., Verma, V., Che, T., and Bengio, Y. (2017) · 2017
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Gibbsnet: Iterative adversarial inference for deep graphical models
Lamb, A. M., Hjelm, D., Ganin, Y., Cohen, J. P., Courville, A. C., and Bengio, Y. (2017) · 2017
Cited alongside, same era.
Deciding how to decide: Dynamic routing in artificial neural networks
McGill, M. and Perona, P. (2017) · 2017
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Routing networks: Adaptive selection of non-linear functions for multi-task learning
Rosenbaum, C., Klinger, T., and Riemer, M. (2017) · 2017
Cited alongside, same era.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., and Dean, J. (2017) · 2017
Cited alongside, same era.
Recurrent convolutional neural networks: a better model of biological object recognition
Spoerer, C. J., McClure, P., and Kriegeskorte, N. (2017) · 2017
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018) · 2018
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Convolutional networks with adaptive inference graphs
Veit, A. and Belongie, S. (2018) · 2018
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Manifold mixup: Better representations by interpolating hidden states
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., and Bengio, Y. (2018) · 2018
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Skipnet: Learning dynamic routing in convolutional networks
Wang, X., Yu, F., Dou, Z.-Y., Darrell, T., and Gonzalez, J. E. (2018) · 2018
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Cbam: Convolutional block attention module
Woo, S., Park, J., Lee, J.-Y., and So Kweon, I. (2018) · 2018
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Feedback networks
Zamir, A. R., Wu, T.-L., Sun, L., Shen, W. B., Shi, B. E., Malik, J., and Savarese, S. (2017) · 2017
Cited alongside, same era.
Dilated densenets for relational reasoning
Antoniou, A., Słowik, A., Crowley, E. J., and Storkey, A. (2018) · 2018
Cited alongside, same era.
Systematic generalization: what is required and can it be learned?
Bahdanau, D., Murty, S., Noukhovitch, M., Nguyen, T. H., de Vries, H., and Courville, A. (2018) · 2018
Cited alongside, same era.
Implicit quantile networks for distributional reinforcement learning
Dabney, W., Ostrovski, G., Silver, D., and Munos, R. (2018) · 2018
Cited alongside, same era.
Rainbow: Combining improvements in deep reinforcement learning
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D. (2018) · 2018
Cited alongside, same era.
Gather-excite: Exploiting feature context in convolutional neural networks
Hu, J., Shen, L., Albanie, S., Sun, G., and Vedaldi, A. (2018) · 2018
Cited alongside, same era.
Sparse attentive backtracking: Temporal credit assignment through reminding
Ke, N. R., GOYAL, A. G. A. P., Bilaniuk, O., Binas, J., Mozer, M. C., Pal, C., and Bengio, Y. (2018) · 2018
Cited alongside, same era.
Blockdrop: Dynamic inference paths in residual networks
Wu, Z., Nagarajan, T., Kumar, A., Rennie, S., Davis, L. S., Grauman, K., and Feris, R. (2018) · 2018
Later among the works it cites.
mixup: Beyond empirical risk minimization. iclr 2018
Zhang, H., Cisse, M., Dauphin, Y., and Lopez-Paz, D. (2017) · 2018
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Self-attention generative adversarial networks
Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2018) · 2018
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Self-supervised gans via auxiliary rotation loss
Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N. (2019) · 2019
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I. (2019) · 2019
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Infomax-gan: Mutual information maximization for improved adversarial image generation
Kwot Sin Lee, N.-T. T. and Cheung, N.-M. (2019) · 2019
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Rltime: A reinforcement learning library for state-of-the-art q-learning
Lieber, O. (2019) · 2019
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Logan: Latent optimisation for generative adversarial networks
Wu, Y., Donahue, J., Balduzzi, D., Simonyan, K., and Lillicrap, T. (2019) · 2019
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Mimicry: Towards the reproducibility of gan research
Lee, K. S. and Town, C. (2020) · 2020
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