Fetching the paper…
Reading the bibliography…
Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes.
Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
Earlier work this paper cites.
Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2015
Earlier work this paper cites.
A recurrent latent variable model for sequential data
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
Earlier work this paper cites.
Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids
Mordatch, I., Lowrey, K., and Todorov, E · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Srivastava, N., Mansimov, E., and Salakhudinov, R · 2015
Earlier work this paper cites.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
Earlier work this paper cites.
State-space models’ dirty little secrets: even simple linear gaussian models can have estimation problems
Auger-Méthé, M., Field, C., Albertsen, C. M., Derocher, A. E., Lewis, M. A., Jonsen, I. D., and Flemming, J. M · 2016
Cited alongside, same era.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
Sequential neural models with stochastic layers
Fraccaro, M., Sønderby, S. K., Paquet, U., and Winther, O · 2016
Cited alongside, same era.
Towards conceptual compression
Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., and Wierstra, D · 2016
Cited alongside, same era.
Deep variational bayes filters: Unsupervised learning of state space models from raw data
Karl, M., Soelch, M., Bayer, J., and van der Smagt, P · 2016
Cited alongside, same era.
Structured inference networks for nonlinear state space models
Krishnan, R. G., Shalit, U., and Sontag, D · 2017
Later among the works it cites.
State space lstm models with particle mcmc inference
Zheng, X., Zaheer, M., Ahmed, A., Wang, Y., Xing, E. P., and Smola, A. J · 2017
Later among the works it cites.
Learning and querying fast generative models for reinforcement learning
Buesing, L., Weber, T., Racaniere, S., Eslami, S., Rezende, D., Reichert, D. P., Viola, F., Besse, F., Gregor, K., Hassabis, D., et al · 2018
Later among the works it cites.
Neural scene representation and rendering
Eslami, S. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al · 2018
Later among the works it cites.
Generative temporal models with spatial memory for partially observed environments
Fraccaro, M., Rezende, D., Zwols, Y., Pritzel, A., Eslami, S. A., and Viola, F · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Identification of gaussian process state space models
Eleftheriadis, S., Nicholson, T., Deisenroth, M., and Hensman, J · 2017
Cited alongside, same era.
A disentangled recognition and nonlinear dynamics model for unsupervised learning
Fraccaro, M., Kamronn, S., Paquet, U., and Winther, O · 2017
Cited alongside, same era.
Generative temporal models with memory
Gemici, M., Hung, C.-C., Santoro, A., Wayne, G., Mohamed, S., Rezende, D. J., Amos, D., and Lillicrap, T · 2017
Cited alongside, same era.
Z-forcing: Training stochastic recurrent networks
Goyal, A. G. A. P., Sordoni, A., Côté, M.-A., Ke, N. R., and Bengio, Y · 2017
Cited alongside, same era.
Garnelo, M., Rosenbaum, D., Maddison, C. J., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., and Eslami, S
Cited in the paper.
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W
Cited in the paper.
Later among the works it cites.
Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2018
Later among the works it cites.
Consistent generative query networks
Kumar, A., Eslami, S., Rezende, D. J., Garnelo, M., Viola, F., Lockhart, E., and Shanahan, M · 2018
Later among the works it cites.
Learning models for visual 3d localization with implicit mapping
Rosenbaum, D., Besse, F., Viola, F., Rezende, D. J., and Eslami, S · 2018
Later among the works it cites.
Kim, H., Mnih, A., Schwarz, J., Garnelo, M., Eslami, A., Rosenbaum, D., Vinyals, O., and Teh, Y. W · 2019
Closest in time.