Fetching the paper…
Reading the bibliography…
Density ratio estimation (DRE) is a fundamental machine learning technique for comparing two probability distributions.
Neutra-lizing bad geometry in hamiltonian monte carlo using neural transport
Hoffman, M., Sountsov, P., Dillon, J. V., Langmore, I., Tran, D., and Vasudevan, S. (2019) · 1903
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019b) · 1907
Earlier work this paper cites.
Understanding the limitations of variational mutual information estimators
Song, J. and Ermon, S. (2019a) · 1910
Earlier work this paper cites.
Efficient estimation of free energy differences from monte carlo data
Bennett, C. H. (1976) · 1976
Earlier work this paper cites.
A family of embedded runge-kutta formulae
Dormand, J. R. and Prince, P. J. (1980) · 1980
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
Neal, R. M. (1993) · 1993
Earlier work this paper cites.
Estimating normalizing constants and reweighting mixtures
Geyer, C. J. (1994) · 1994
Earlier work this paper cites.
Simulating ratios of normalizing constants via a simple identity: a theoretical exploration
Meng, X.-L. and Wong, W. H. (1996) · 1996
Earlier work this paper cites.
Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
Gelman, A. and Meng, X.-L. (1998) · 1998
Earlier work this paper cites.
The variational formulation of the fokker–planck equation
Jordan, R., Kinderlehrer, D., and Otto, F. (1998) · 1998
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y. (1998) · 1998
Earlier work this paper cites.
Annealed importance sampling
Neal, R. M. (2001) · 2001
Earlier work this paper cites.
Stochastic differential equations
Øksendal, B. (2003) · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A. (2005) · 2005
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2006
Earlier work this paper cites.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S. (2020) · 2006
Earlier work this paper cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., and Ng, R. (2020) · 2006
Earlier work this paper cites.
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I. (2007) · 2007
Earlier work this paper cites.
Direct importance estimation for covariate shift adaptation
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., and Kawanabe, M. (2008) · 2008
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B. (2009) · 2009
Cited alongside, same era.
Adaptive path sampling in metastable posterior distributions
Yao, Y., Cademartori, C., Vehtari, A., and Gelman, A. (2020) · 2009
Cited alongside, same era.
Regularized estimation of image statistics by score matching
Kingma, D. P. and LeCun, Y. (2010) · 2010
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
Cited alongside, same era.
Bregman divergence as general framework to estimate unnormalized statistical models
Gutmann, M. and Hirayama, J.-i. (2012) · 2012
Cited alongside, same era.
Mutual information neural estimation
Belghazi, M. I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., and Hjelm, D. (2018) · 2018
Later among the works it cites.
Conditional noise-contrastive estimation of unnormalised models
Ceylan, C. and Gutmann, M. U. (2018) · 2018
Later among the works it cites.
The sample size required in importance sampling
Chatterjee, S. and Diaconis, P. (2018) · 2018
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. (2018) · 2018
Later among the works it cites.
Learning weighted representations for generalization across designs
Johansson, F. D., Kallus, N., Shalit, U., and Sontag, D. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fishman, G. (2013) · 2013
Cited alongside, same era.
Monte carlo theory, methods and examples
Owen, A. B. (2013) · 2013
Cited alongside, same era.
Relative density-ratio estimation for robust distribution comparison
Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., and Sugiyama, M. (2013) · 2013
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Accurate and conservative estimates of mrf log-likelihood using reverse annealing
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015) · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Cited alongside, same era.
Oord, A. v. d., Li, Y., and Vinyals, O. (2018) · 2018
Later among the works it cites.
Group normalization
Wu, Y. and He, K. (2018) · 2018
Later among the works it cites.
Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019) · 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., et al. (2019) · 2019
Later among the works it cites.
On variational bounds of mutual information
Poole, B., Ozair, S., Van Den Oord, A., Alemi, A., and Tucker, G. (2019) · 2019
Later among the works it cites.
Formal limitations on the measurement of mutual information
McAllester, D. and Stratos, K. (2020) · 2020
Later among the works it cites.
Telescoping density-ratio estimation
Rhodes, B., Xu, K., and Gutmann, M. U. (2020) · 2020
Later among the works it cites.
Neural bridge sampling for evaluating safety-critical autonomous systems
Sinha, A., O’Kelly, M., Tedrake, R., and Duchi, J. C. (2020) · 2020
Later among the works it cites.
Sliced score matching: A scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S. (2020) · 2020
Later among the works it cites.
Featurized density ratio estimation
Choi, K., Liao, M., and Ermon, S. (2021) · 2021
Closest in time.
Kingma, D. P., Salimans, T., Poole, B., and Ho, J. (2021) · 2021
Closest in time.
Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P. (2021) · 2021
Closest in time.
Should ebms model the energy or the score?
Salimans, T. and Ho, J. (2021) · 2021
Closest in time.