Fetching the paper…
Reading the bibliography…
Density deconvolution is the task of estimating a probability density function given only noise-corrupted samples.
Use of Different Monte Carlo Sampling Techniques
Kahn, H · 1955
Earlier work this paper cites.
Optimal Rates of Convergence for Deconvolving a Density
Carroll, R. J. and Hall, P · 1988
Earlier work this paper cites.
Using the SIR Algorithm to Simulate Posterior Distributions
Rubin, D. B · 1988
Earlier work this paper cites.
Consistent Deconvolution in Density Estimation
Devroye, L · 1989
Earlier work this paper cites.
A Consistent Nonparametric Density Estimator for the Deconvolution Problem
Liu, M. C. and Taylor, R. L · 1989
Earlier work this paper cites.
On the Optimal Rates of Convergence for Nonparametric Deconvolution Problems
Fan, J · 1991
Earlier work this paper cites.
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
Earlier work this paper cites.
Adaptive Wavelet Estimator for Nonparametric Density Deconvolution
Pensky, M. and Vidakovic, B · 1999
Earlier work this paper cites.
Measurement Error in Nonlinear Models: A Modern Perspective
Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M · 2006
Earlier work this paper cites.
NumPy: A Guide to NumPy
Oliphant, T · 2006
Earlier work this paper cites.
Matplotlib: A 2D Graphics Environment
Hunter, J. D · 2007
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
McKinney, W · 2010
Earlier work this paper cites.
Density Estimation by Dual Ascent of the Log-Likelihood
Tabak, E. G. and Vanden-Eijnden, E · 2010
Earlier work this paper cites.
Extreme Deconvolution: Inferring Complete Distribution Functions from Noisy, Heterogeneous and Incomplete Observations
Bovy, J., Hogg, D. W., and Roweis, S. T · 2011
Earlier work this paper cites.
A Family of Nonparametric Density Estimation Algorithms
Tabak, E. G. and Turner, C. V · 2013
Earlier work this paper cites.
RNADE: The Real-Valued Neural Autoregressive Density-Estimator
Uria, B., Murray, I., and Larochelle, H · 2013
Cited alongside, same era.
Exact Estimation for Markov Chain Equilibrium Expectations
Glynn, P. W. and Rhee, C.-h · 2014
Cited alongside, same era.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
Cited alongside, same era.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Cited alongside, same era.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Importance Weighted Autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
UCI Machine Learning Repository, 2017
Dua, D. and Graff, C · 2017
Later among the works it cites.
Masked Autoregressive Flow for Density Estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Later among the works it cites.
Neural Discrete Representation Learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
Later among the works it cites.
Improving Gaia Parallax Precision with a Data-Driven Model of Stars
Anderson, L., Hogg, D. W., Leistedt, B., Price-Whelan, A. M., and Bovy, J · 2018
Later among the works it cites.
Inference Suboptimality in Variational Autoencoders
Cremer, C., Li, X., and Duvenaud, D · 2018
Later among the works it cites.
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates
Cranmer, M. D., Galvez, R., Anderson, L., Spergel, D. N., and Ho, S · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
MADE: Masked Autoencoder for Distribution Estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Variational Inference with Normalizing Flows
Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
Dilokthanakul, N., Mediano, P. A., Garnelo, M., Lee, M. C., Salimbeni, H., Arulkumaran, K., and Shanahan, M · 2016
Cited alongside, same era.
corner.py
Foreman-Mackey, D · 2016
Cited alongside, same era.
Improved Variational Inference with Inverse Autoregressive Flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Cited alongside, same era.
Later among the works it cites.
Autoregressive Energy Machines
Durkan, C. and Nash, C · 2019
Later among the works it cites.
Neural Spline Flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
Later among the works it cites.
Sum-of-Squares Polynomial Flow
Jaini, P., Selby, K. A., and Yu, Y · 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.
Pytorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Later among the works it cites.
Scalable Extreme Deconvolution
Ritchie, J. A. and Murray, I · 2019
Later among the works it cites.
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Luo, Y., Beatson, A., Norouzi, M., Zhu, J., Duvenaud, D., Adams, R. P., and Chen, R. T. Q · 2020
Closest in time.
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
Qiu, Y., Zhang, L., and Wang, X · 2020
Closest in time.