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Modeling sets is an important problem in machine learning since this type of data can be found in many domains.
La prévision: ses lois logiques, ses sources subjectives
De Finetti, B · 1937
Earlier work this paper cites.
A generalization of poisson’s binomial limit for use in ecology
Thomas, M · 1949
Earlier work this paper cites.
Some statistical methods connected with series of events
Cox, D. R · 1955
Earlier work this paper cites.
Statistical approach to problems of cosmology
Neyman, J. and Scott, E. L · 1958
Earlier work this paper cites.
Statistical mechanics: Rigorous results
Ruelle, D · 1969
Earlier work this paper cites.
Statistical analysis of non-lattice data
Besag, J · 1975
Earlier work this paper cites.
A family of embedded runge-kutta formulae
Dormand, J. R. and Prince, P. J · 1980
Earlier work this paper cites.
Estimation of interaction potentials of marked spatial point patterns through the maximum likelihood method
Ogata, Y. and Tanemura, M · 1985
Earlier work this paper cites.
Logistic regression for spatial pair-potential models
Clyde, M. and Strauss, D · 1991
Earlier work this paper cites.
Improvements of the maximum pseudo-likelihood estimators in various spatial statistical models
Huang, F. and Ogata, Y · 1999
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E. and Williams, C. K. I · 2006
Earlier work this paper cites.
An introduction to the theory of point processes: Volume I: Elementary theory and methods
Daley, D. and Vere-Jones, D · 2007
Earlier work this paper cites.
Exchangeability, correlation, and bayes’ effect
O’Neill, B · 2009
Earlier work this paper cites.
Friendship and mobility: user movement in location-based social networks
Cho, E., Myers, S. A., and Leskovec, J · 2011
Earlier work this paper cites.
Stochastic geometry and its applications
Chiu, S. N., Stoyan, D., Kendall, W. S., and Mecke, J · 2013
Earlier work this paper cites.
Automated derivation of the adjoint of high-level transient finite element programs
Farrell, P. E., Ham, D. A., Funke, S. W., and Rognes, M. E · 2013
Earlier work this paper cites.
Spatial variation , volume 36
Matérn, B · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Variational inference for gaussian process modulated poisson processes
Lloyd, C., Gunter, T., Osborne, M., and Roberts, S · 2015
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
Cited alongside, same era.
RMTPP: Embedding event history to vector
Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., and Song, L · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Grover, A. and Leskovec, J · 2016
Anode: Unconditionally accurate memory-efficient gradients for neural odes
Gholami, A., Keutzer, K., and Biros, G · 2019
Later among the works it cites.
FFJORD: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Bettencourt, J., Sutskever, I., and Duvenaud, D · 2019
Later among the works it cites.
Set transformer: A framework for attention-based permutation-invariant neural networks
Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., and Teh, Y. W · 2019
Later among the works it cites.
Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Murphy, R. L., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
Later among the works it cites.
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
Later among the works it cites.
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Cited alongside, same era.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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