Fetching the paper…
Reading the bibliography…
A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, W. R · 1933
Earlier work this paper cites.
La prévision: ses lois logiques, ses sources subjectives
De Finetti, B · 1937
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Stochastic differential equations
Øksendal, B · 2003
Earlier work this paper cites.
A unifying view of sparse approximate gaussian process regression
Quiñonero-Candela, J. and Rasmussen, C. E · 2005
Earlier work this paper cites.
Gaussian process regression networks
Wilson, A. G., Knowles, D. A., and Ghahramani, Z · 2011
Earlier work this paper cites.
Analysis of thompson sampling for the multi-armed bandit problem
Agrawal, S. and Goyal, N · 2012
Earlier work this paper cites.
Deep gaussian processes
Damianou, A. and Lawrence, N · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 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.
Scalable gaussian process regression using deep neural networks
Huang, W.-b., Zhao, D., Sun, F., Liu, H., and Chang, E. Y · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Koch, G., Zemel, R., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
Cited alongside, same era.
Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X · 2015
Cited alongside, same era.
Kernel interpolation for scalable structured gaussian processes (kiss-gp)
Wilson, A. and Nickisch, H · 2015
Cited alongside, same era.
Fast adaptation in generative models with generative matching networks
Bartunov, S. and Vetrov, D. P · 2016
Cited alongside, same era.
Manifold gaussian processes for regression
Calandra, R., Peters, J., Rasmussen, C. E., and Deisenroth, M. P · 2016
Cited alongside, same era.
Neural program meta-induction
Devlin, J., Bunel, R. R., Singh, R., Hausknecht, M., and Kohli, P · 2017
Later among the works it cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Later among the works it cites.
Multiplicative normalizing flows for variational bayesian neural networks
Louizos, C. and Welling, M · 2017
Later among the works it cites.
Bayesian compression for deep learning
Louizos, C., Ullrich, K., and Welling, M · 2017
Later among the works it cites.
Few-shot autoregressive density estimation: Towards learning to learn distributions
Reed, S., Chen, Y., Paine, T., Oord, A. v. d., Eslami, S., J. Rezende, D., Vinyals, O., and de Freitas, N · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Edwards, H. and Storkey, A · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
One-shot generalization in deep generative models
Rezende, D. J., Mohamed, S., Danihelka, I., Gregor, K., and Wierstra, D · 2016
Cited alongside, same era.
One-shot learning with memory-augmented neural networks
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T · 2016
Cited alongside, same era.
Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
Cited alongside, same era.
Snell, J., Swersky, K., and Zemel, R · 2017
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
Closest in time.
Probabilistic model-agnostic meta-learning
Finn, C., Xu, K., and Levine, S · 2018
Closest in time.
Conditional neural processes
Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D. J., and Eslami, A · 2018
Closest in time.
Recasting gradient-based meta-learning as hierarchical bayes
Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T · 2018
Closest in time.
The variational homoencoder: Learning to infer high-capacity generative models from few examples
Hewitt, L., Gane, A., Jaakkola, T., and Tenenbaum, J. B · 2018
Closest in time.
Consistent generative query networks
Kumar, A., Eslami, S. M. A., Rezende, D. J., Garnelo, M., Viola, F., Lockhart, E., and Shanahan, M · 2018
Closest in time.
Variational implicit processes
Ma, C., Li, Y., and Hernández-Lobato, J. M · 2018
Closest in time.
Riquelme, C., Tucker, G., and Snoek, J · 2018
Closest in time.
Differentiable compositional kernel learning for gaussian processes
Sun, S., Zhang, G., Wang, C., Zeng, W., Li, J., and Grosse, R · 2018
Closest in time.