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We introduce the Generalized Energy Based Model (GEBM) for generative modelling.
Exponential Family Estimation via Adversarial Dynamics Embedding
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LOGAN: Latent Optimisation for Generative Adversarial Networks
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Convex analysis
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Asymptotic evaluation of certain markov process expectations for large time, i
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Importance sampling in the monte carlo study of sequential tests
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Review: J. diestel and j. j. uhl, jr., vector measures
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Convex Analysis and Variational Problems
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Sequential Monte Carlo Methods in Practice
Doucet, A., Freitas, N. d., and Gordon, N. (2001) · 2001
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Training products of experts by minimizing contrastive divergence
Hinton, G. E. (2002) · 2002
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Envelope Theorems for Arbitrary Choice Sets
Milgrom, P. and Segal, I. (2002) · 2002
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Simsekli, U., Zhu, L., Teh, Y. W., and Gurbuzbalaban, M. (2020) · 2002
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Flows for simultaneous manifold learning and density estimation
Brehmer, J. and Cranmer, K. (2020) · 2003
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Residual energy-based models for text generation
Deng, Y., Bakhtin, A., Ott, M., Szlam, A., and Ranzato, M. (2020) · 2004
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Estimation of Non-Normalized Statistical Models by Score Matching
Hyvärinen, A. (2005) · 2005
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Sequential monte carlo samplers
Del Moral, P., Doucet, A., and Jasra, A. (2006) · 2006
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Predicting Structured Data
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F.-J. (2006) · 2006
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Probability Theory: A Comprehensive Course
Klenke, A. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Direct density ratio estimation for large-scale covariate shift adaptation
Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., and Sugiyama, M. (2009) · 2009
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Optimal transport: Old and new
Villani, C. (2009) · 2009
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Mcmc using hamiltonian dynamics
Neal, R. M. (2010) · 2010
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Estimating divergence functionals and the likelihood ratio by convex risk minimization
Nguyen, X., Wainwright, M. J., and Jordan, M. I. (2010) · 2010
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Duchi, J., Hazan, E., and Singer, Y. (2011) · 2011
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f f -divergence estimation and two-sample homogeneity test under semiparametric density-ratio models
Kanamori, T., Suzuki, T., and Sugiyama, M. (2011) · 2011
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, M. U. and Hyvärinen, A. (2012) · 2012
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Density ratio estimation in machine learning
Sugiyama, M., Suzuki, T., and Kanamori, T. (2012) · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2014) · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S. (2015) · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J. (2015) · 2015
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Structured prediction energy networks
Belanger, D. and McCallum, A. (2016) · 2016
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Approximating likelihood ratios with calibrated discriminative classifiers
Cranmer, K., Pavez, J., and Louppe, G. (2016) · 2016
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Density estimation using real nvp
Kernel Conditional Exponential Family
Arbel, M. and Gretton, A. (2018) · 2018
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On gradient regularizers for mmd gans
Arbel, M., Sutherland, D., Binkowski, M., and Gretton, A. (2018) · 2018
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Demystifying MMD GANs
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A. (2018) · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2018) · 2018
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Stochastic subgradient method converges at the rate $O(k^{-1/4})$ on weakly convex functions
Davis, D. and Drusvyatskiy, D. (2018) · 2018
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Spectral normalization for generative adversarial networks
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Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016) · 2016
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Generative adversarial imitation learning
Ho, J. and Ermon, S. (2016) · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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Pixel recurrent neural networks
Oord, A. v. d., Kalchbrenner, N., and Kavukcuoglu, K. (2016) · 2016
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A theory of generative convnet
Xie, J., Lu, Y., Zhu, S.-C., and Wu, Y. (2016) · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y. (2017) · 2017
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Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018) · 2018
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Autoregressive quantile networks for generative modeling
Ostrovski, G., Dabney, W., and Munos, R. (2018) · 2018
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On the convergence and robustness of training gans with regularized optimal transport
Sanjabi, M., Ba, J., Razaviyayn, M., and Lee, J. D. (2018) · 2018
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Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions
Simon-Gabriel, C.-J. and Scholkopf, B. (2018) · 2018
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Efficient and principled score estimation with Nystrom kernel exponential families
Sutherland, D., Strathmann, H., Arbel, M., and Gretton, A. (2018) · 2018
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Learning approximate inference networks for structured prediction
Tu, L. and Gimpel, K. (2018) · 2018
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Learning implicit generative models by teaching density estimators
Xu, K., Du, C., Li, C., Zhu, J., and Zhang, B. (2018) · 2018
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Kernelized Wasserstein Natural Gradient
Arbel, M., Gretton, A., Li, W., and Montufar, G. (2019) · 2019
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Discriminator rejection sampling
Azadi, S., Olsson, C., Darrell, T., Goodfellow, I., and Odena, A. (2019) · 2019
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Self-supervised gans via auxiliary rotation loss
Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N. (2019) · 2019
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Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss
Ding, X., Wang, Z. J., and Welch, W. J. (2019) · 2019
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Implicit generation and modeling with energy based models
Du, Y. and Mordatch, I. (2019) · 2019
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Interpolating between optimal transport and mmd using sinkhorn divergences
Feydy, J., Séjourné, T., Vialard, F.-X., Amari, S.-i., Trouvé, A., and Peyré, G. (2019) · 2019
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Bias correction of learned generative models using likelihood-free importance weighting
Grover, A., Song, J., Kapoor, A., Tran, K., Agarwal, A., Horvitz, E. J., and Ermon, S. (2019) · 2019
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Energy-inspired models: Learning with sampler-induced distributions
Lawson, J., Tucker, G., Dai, B., and Ranganath, R. (2019) · 2019
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The implicit metropolis-hastings algorithm
Neklyudov, K., Egorov, E., and Vetrov, D. (2019) · 2019
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Discriminator optimal transport
Tanaka, A. (2019) · 2019
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Efficient algorithms for smooth minimax optimization
Thekumparampil, K. K., Jain, P., Netrapalli, P., and Oh, S. (2019) · 2019
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Metropolis-Hastings generative adversarial networks
Turner, R., Hung, J., Frank, E., Saatchi, Y., and Yosinski, J. (2019) · 2019
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Learning deep kernels for exponential family densities
Wenliang, L., Sutherland, D., Strathmann, H., and Gretton, A. (2019) · 2019
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Learning energy-based spatial-temporal generative convnets for dynamic patterns
Xie, J., Zhu, S.-C., and Wu, Y. N. (2019) · 2019
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Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
Che, T., Zhang, R., Sohl-Dickstein, J., Larochelle, H., Paull, L., Cao, Y., and Bengio, Y. (2020) · 2020
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Smoothness and stability in gans
Chu, C., Minami, K., and Fukumizu, K. (2020) · 2020
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Relaxing bijectivity constraints with continuously indexed normalising flows
Cornish, R., Caterini, A. L., Deligiannidis, G., and Doucet, A. (2020) · 2020
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Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., Norouzi, M., and Swersky, K. (2020) · 2020
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Training deep energy-based models with f-divergence minimization
Yu, L., Song, Y., Song, J., and Ermon, S. (2020) · 2020
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The unreasonable effectiveness of patches in deep convolutional kernels methods
Thiry, L., Arbel, M., Belilovsky, E., and Oyallon, E. (2021) · 2021
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