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Generative models have shown strong generation ability while efficient likelihood estimation is less explored.
An invariant form for the prior probability in estimation problems
Jeffreys, H · 1946
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Neural networks and physical systems with emergent collective computational abilities
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Optimal perceptual inference
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Information Processing in Dynamical Systems: Foundations of Harmony Theory , pp. 194–281
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Crafting papers on machine learning
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Information theory
Orlitsky, A · 2003
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Estimation of the entropy of a multivariate normal distribution
Misra, N., Singh, H., and Demchuk, E · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 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
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Generative adversarial networks as variational training of energy based models
Zhai, S., Cheng, Y., Feris, R., and Zhang, Z · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M., and Sutton, C · 2017
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2017
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Symmetric variational autoencoder and connections to adversarial learning
Chen, L., Dai, S., Pu, Y., Zhou, E., Li, C., Su, Q., Chen, C., and Carin, L · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Which training methods for gans do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Prescribed generative adversarial networks
Dieng, A. B., Ruiz, F. J., Blei, D. M., and Titsias, M. K · 2019
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Implicit generation and generalization in energy-based models
Du, Y. and Mordatch, I · 2019
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Divergence triangle for joint training of generator model, energy-based model, and inferential model
Han, T., Nijkamp, E., Fang, X., Hill, M., Zhu, S.-C., and Wu, Y. N · 2019
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Learning energy-based models by diffusion recovery likelihood
Gao, R., Song, Y., Poole, B., Wu, Y. N., and Kingma, D. P · 2021
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Bounds all around: training energy-based models with bidirectional bounds
Geng, C., Wang, J., Gao, Z., Frellsen, J., and Hauberg, S · 2021
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No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Grathwohl, W. S., Kelly, J. J., Hashemi, M., Norouzi, M., Swersky, K., and Duvenaud, D · 2021
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Adversarial score matching and improved sampling for image generation
Jolicoeur-Martineau, A., Piché-Taillefer, R., Combes, R. T. d., and Mitliagkas, I · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Vaebm: A symbiosis between variational autoencoders and energy-based models
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Kumar, R., Ozair, S., Goyal, A., Courville, A., and Bengio, Y · 2019
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Coco-gan: Generation by parts via conditional coordinating
Lin, C. H., Chang, C.-C., Chen, Y.-S., Juan, D.-C., Wei, W., and Chen, H.-T · 2019
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Learning non-convergent non-persistent short-run mcmc toward energy-based model
Nijkamp, E., Hill, M., Zhu, S.-C., and Wu, Y. N · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Flow contrastive estimation of energy-based models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N · 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
Cited alongside, same era.
Joint training of variational auto-encoder and latent energy-based model
Han, T., Nijkamp, E., Zhou, L., Pang, B., Zhu, S.-C., and Wu, Y. N · 2020
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Xiao, Z., Kreis, K., Kautz, J., and Vahdat, A · 2021
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Learning energy-based model with variational auto-encoder as amortized sampler
Xie, J., Zheng, Z., and Li, P · 2021
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Understanding failures in out-of-distribution detection with deep generative models
Zhang, L., Goldstein, M., and Ranganath, R · 2021
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Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Bao, F., Li, C., Zhu, J., and Zhang, B · 2022
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Learning iterative reasoning through energy minimization
Du, Y., Li, S., Tenenbaum, J., and Mordatch, I · 2022
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Learning probabilistic models from generator latent spaces with hat ebm
Hill, M., Nijkamp, E., Mitchell, J., Pang, B., and Zhu, S.-C · 2022
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Bi-level doubly variational learning for energy-based latent variable models
Kan, G., Lü, J., Wang, T., Zhang, B., Zhu, A., Huang, L., Guo, G., and Snoussi, H · 2022
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Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Liu, R., Gao, J., Zhang, J., Meng, D., and Lin, Z · 2022
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Tackling the generative learning trilemma with denoising diffusion gans
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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A tale of two flows: Cooperative learning of langevin flow and normalizing flow toward energy-based model
Xie, J., Zhu, Y., Li, J., and Li, P · 2022
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Energy-based generative cooperative saliency prediction
Zhang, J., Xie, J., Zheng, Z., and Barnes, N · 2022
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Learning energy-based model via dual-mcmc teaching
Cui, J. and Han, T · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Du, Y., Durkan, C., Strudel, R., Tenenbaum, J. B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W. S · 2023
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Act: Adversarial consistency models
Kong, F., Duan, J., Sun, L., Cheng, H., Xu, R., Shen, H., Zhu, X., Shi, X., and Xu, K · 2023
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Guiding energy-based models via contrastive latent variables
Lee, H., Jeong, J., Park, S., and Shin, J · 2023
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Learning energy-based prior model with diffusion-amortized mcmc
Yu, P., Zhu, Y., Xie, S., Ma, X., Gao, R., Zhu, S.-C., and Wu, Y. N · 2023
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Learning energy-based models by cooperative diffusion recovery likelihood
Zhu, Y., Xie, J., Wu, Y., and Gao, R · 2024
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