Exploring bidirectional bounds for minimax-training of Energy-based models | Paper Citation Record

Observation 5272ff05-1130-44e1-8193-99475a4ed1de · outbound

This paper cites A gen- erative adversarial density estimator.

Exploring bidirectional bounds for minimax-training of Energy-based models A gen- erative adversarial density estimator

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Observation 7b5cb76e-0bcb-4adc-a946-936ea0e79a92 · outbound

This paper cites Gade: A generative adversarial approach to density estimation and its applications.Interna- tional Journal of Computer Vision, 128(10): 2731–2743, 2020.

Exploring bidirectional bounds for minimax-training of Energy-based models Gade: A generative adversarial approach to density estimation and its applications.Interna- tional Journal of Computer Vision, 128(10): 2731–2743, 2020

Reference 2

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This paper cites Ackley, Geoffrey E.

Exploring bidirectional bounds for minimax-training of Energy-based models Ackley, Geoffrey E

Reference 3

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This paper cites Uncertainty in the variational informa- tion bottleneck.

Exploring bidirectional bounds for minimax-training of Energy-based models Uncertainty in the variational informa- tion bottleneck

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This paper cites Wasserstein generative adversar- ial networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Wasserstein generative adversar- ial networks

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This paper cites Variational inference: A review for statisticians.Journal of the American statis- tical Association, 112(518):859–877, 2017.

Exploring bidirectional bounds for minimax-training of Energy-based models Variational inference: A review for statisticians.Journal of the American statis- tical Association, 112(518):859–877, 2017

Reference 6

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This paper cites Accurate and conservative estimates of MRF log-likelihood using reverse annealing.

Exploring bidirectional bounds for minimax-training of Energy-based models Accurate and conservative estimates of MRF log-likelihood using reverse annealing

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This paper cites Your GAN is secretly an energy-based model and you should use discriminator driven latent sam- pling.

Exploring bidirectional bounds for minimax-training of Energy-based models Your GAN is secretly an energy-based model and you should use discriminator driven latent sam- pling

Reference 8

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Observation 4272db0a-ddc2-4c25-90d3-3226807afdf8 · outbound

This paper cites WAIC, but Why? Generative Ensembles for Robust Anomaly Detection.

Exploring bidirectional bounds for minimax-training of Energy-based models WAIC, but Why? Generative Ensembles for Robust Anomaly Detection

Reference 9

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Observation 3d9e3240-41e0-44d2-9e8e-a2c31223e9af · outbound

This paper cites Generative modeling through the semi-dual formulation of unbalanced optimal transport.Advances in Neural Information Processing Systems, 36, 2024.

Exploring bidirectional bounds for minimax-training of Energy-based models Generative modeling through the semi-dual formulation of unbalanced optimal transport.Advances in Neural Information Processing Systems, 36, 2024

Reference 10

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This paper cites Calibrating energy-based generative adversar- ial networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Calibrating energy-based generative adversar- ial networks

Reference 11

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Observation 30d86d52-a1ff-4ec2-b99d-1b8d939309ab · outbound

This paper cites Prescribed Generative Adversarial Networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Prescribed Generative Adversarial Networks

Reference 12

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Observation a4f2f22e-6fe5-42d3-93fd-8fb18f574edd · outbound

This paper cites Nice: Non-linear independent compo- nents estimation.

Exploring bidirectional bounds for minimax-training of Energy-based models Nice: Non-linear independent compo- nents estimation

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Observation ac728cca-2686-4e7a-9541-bcf337b12ed0 · outbound

This paper cites Implicit genera- tion and modeling with energy based models.

Exploring bidirectional bounds for minimax-training of Energy-based models Implicit genera- tion and modeling with energy based models

Reference 14

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Observation 784b93d1-2380-46ff-a37e-ad1354df0f5d · outbound

This paper cites Bayesian generalised ensemble markov chain monte Springer Nature 2021 LATEX template 22Article Title carlo.

Exploring bidirectional bounds for minimax-training of Energy-based models Bayesian generalised ensemble markov chain monte Springer Nature 2021 LATEX template 22Article Title carlo

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Observation ee878fd4-70d2-4aa5-b48c-c93f4082a371 · outbound

This paper cites Learning energy-based models by diffusion recovery like- lihood.International Conference on Learning Representations, 2021.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning energy-based models by diffusion recovery like- lihood.International Conference on Learning Representations, 2021

Reference 16

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Observation d7a88396-2eda-4ec1-bcab-59e068af8e6c · outbound

This paper cites Bounds all around: training energy-based models with bidirectional bounds.Advances in Neural Information Processing Systems, 34:19808– 19821, 2021.

Exploring bidirectional bounds for minimax-training of Energy-based models Bounds all around: training energy-based models with bidirectional bounds.Advances in Neural Information Processing Systems, 34:19808– 19821, 2021

Reference 17

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Observation cbba445c-f60c-457e-a233-94363fd5cebb · outbound

This paper cites Improving adversarial energy-based model via diffusion process.Proceedings of the 41th International Conference on Machine Learning, 2024.

Exploring bidirectional bounds for minimax-training of Energy-based models Improving adversarial energy-based model via diffusion process.Proceedings of the 41th International Conference on Machine Learning, 2024

Reference 18

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Observation 3c54aefa-bc92-4e34-b754-9a5c2bd98fb8 · outbound

This paper cites Generative adversarial networks.Communi- cations of the ACM, 63(11):139–144, 2020.

Exploring bidirectional bounds for minimax-training of Energy-based models Generative adversarial networks.Communi- cations of the ACM, 63(11):139–144, 2020

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Observation f9a22b75-05a1-48f8-9f4f-dc2d97e858d8 · outbound

This paper cites No MCMC for me: Amortized sampling for fast and stable training of energy-based models.

Exploring bidirectional bounds for minimax-training of Energy-based models No MCMC for me: Amortized sampling for fast and stable training of energy-based models

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Observation 736b53c4-b52a-47c6-a459-999fdc6af415 · outbound

This paper cites Annealing between dis- tributions by averaging moments.Advances in Neural Information Processing Systems, 26, 2013.

Exploring bidirectional bounds for minimax-training of Energy-based models Annealing between dis- tributions by averaging moments.Advances in Neural Information Processing Systems, 26, 2013

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Observation 76bbedee-fd48-4b34-ace2-633a93065d2f · outbound

This paper cites Improved training of wasserstein gans.Advances in neural information process- ing systems, 30, 2017.

Exploring bidirectional bounds for minimax-training of Energy-based models Improved training of wasserstein gans.Advances in neural information process- ing systems, 30, 2017

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This paper cites Noise-contrastive estimation: A new estima- tion principle for unnormalized statistical models.

Exploring bidirectional bounds for minimax-training of Energy-based models Noise-contrastive estimation: A new estima- tion principle for unnormalized statistical models

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Observation e00bc4c1-b0c7-4a26-a62c-f34f53dcc8de · outbound

This paper cites Divergence triangle for joint training of genera- tor model, energy-based model, and inferential model.

Exploring bidirectional bounds for minimax-training of Energy-based models Divergence triangle for joint training of genera- tor model, energy-based model, and inferential model

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Observation da05d473-44c5-476c-a8ad-831cc0c8390c · outbound

This paper cites Joint training of variational auto-encoder and latent energy-based model.

Exploring bidirectional bounds for minimax-training of Energy-based models Joint training of variational auto-encoder and latent energy-based model

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Observation 13e2bbaa-ca2d-4bd5-9273-5ca844ad6a41 · outbound

This paper cites Hierarchical vaes know what they don’t know.

Exploring bidirectional bounds for minimax-training of Energy-based models Hierarchical vaes know what they don’t know

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Observation e9f31569-35c1-496e-9fc0-d7eb78917502 · outbound

This paper cites A base- line for detecting misclassified and out-of- distribution examples in neural networks.

Exploring bidirectional bounds for minimax-training of Energy-based models A base- line for detecting misclassified and out-of- distribution examples in neural networks

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Observation bf1a7d2b-57a2-421e-9afe-6bf34b05d7f4 · outbound

This paper cites Deep anomaly detection with outlier exposure.

Exploring bidirectional bounds for minimax-training of Energy-based models Deep anomaly detection with outlier exposure

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Observation 1b72d5b7-a6f2-46af-b8b3-520810f26140 · outbound

This paper cites Training products of experts by minimizing contrastive divergence.

Exploring bidirectional bounds for minimax-training of Energy-based models Training products of experts by minimizing contrastive divergence

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Observation 467b4cbb-fa61-4c7e-9de6-49b34f82a6c7 · outbound

This paper cites Optimal perceptual inference.

Exploring bidirectional bounds for minimax-training of Energy-based models Optimal perceptual inference

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Observation d1348b56-a921-4c35-8186-b66b712d4fb7 · outbound

This paper cites A fast learning algorithm for deep Springer Nature 2021 LATEX template Article Title23 belief nets.Neural computation, 18(7):1527– 1554, 2006.

Exploring bidirectional bounds for minimax-training of Energy-based models A fast learning algorithm for deep Springer Nature 2021 LATEX template Article Title23 belief nets.Neural computation, 18(7):1527– 1554, 2006

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Observation 7cdd7dc1-cb57-4995-9560-1e7ca5e0c3f6 · outbound

This paper cites Learning deep representations by mutual information estimation and maximization.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning deep representations by mutual information estimation and maximization

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Observation c7cd4383-4a4f-4f88-8b6a-a26887ff0eae · outbound

This paper cites Denoising diffusion probabilistic models.

Exploring bidirectional bounds for minimax-training of Energy-based models Denoising diffusion probabilistic models

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Observation 8daf3f14-f75d-42a2-82a7-54714b347c19 · outbound

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Exploring bidirectional bounds for minimax-training of Energy-based models Neural networks and physical systems with emergent collective computa- tional abilities.Proceedings of the National Academy of Sciences, 79(8):2554–2558, 1982

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Observation f059c2a0-2a6e-468a-a29e-9b07b5d14452 · outbound

This paper cites A stochastic estimator of the trace of the influence matrix for lapla- cian smoothing splines.Communications in Statistics-Simulation and Computation, 18(3): 1059–1076, 1989.

Exploring bidirectional bounds for minimax-training of Energy-based models A stochastic estimator of the trace of the influence matrix for lapla- cian smoothing splines.Communications in Statistics-Simulation and Computation, 18(3): 1059–1076, 1989

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Observation f39c247f-b536-49e6-996a-bb32e7b499ed · outbound

This paper cites Estimation of non- normalized statistical models by score match- ing.Journal of Machine Learning Research, 6 (4), 2005.

Exploring bidirectional bounds for minimax-training of Energy-based models Estimation of non- normalized statistical models by score match- ing.Journal of Machine Learning Research, 6 (4), 2005

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Observation 83753f75-3da3-4fcd-886b-73deeedd38c4 · outbound

This paper cites Bi-level doubly varia- tional learning for energy-based latent variable models.

Exploring bidirectional bounds for minimax-training of Energy-based models Bi-level doubly varia- tional learning for energy-based latent variable models

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Observation b06106df-26d7-4dcf-837a-b811bbbc3959 · outbound

This paper cites ContraGAN: Contrastive learning for conditional image generation.

Exploring bidirectional bounds for minimax-training of Energy-based models ContraGAN: Contrastive learning for conditional image generation

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Observation c4006330-b5d9-43de-90c0-1b45376629e0 · outbound

This paper cites Soft truncation: A universal training technique of score-based diffusion model for high preci- sion score estimation.

Exploring bidirectional bounds for minimax-training of Energy-based models Soft truncation: A universal training technique of score-based diffusion model for high preci- sion score estimation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.577561Z

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Observation 00f12c1f-e6bf-45e9-886a-1879a6dfd0fa · outbound

This paper cites Deep Directed Generative Models with Energy-Based Probability Estimation.

Exploring bidirectional bounds for minimax-training of Energy-based models Deep Directed Generative Models with Energy-Based Probability Estimation

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Observation 4fbd8ecc-a4b8-429b-acbe-e645a3fbbf45 · outbound

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Exploring bidirectional bounds for minimax-training of Energy-based models Toward the optimal pre- conditioned eigensolver: Locally optimal block preconditioned conjugate gradient method

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.567685Z

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Observation 4f9e193a-9435-4be8-896f-fff87a294540 · outbound

This paper cites Learning multiple layers of features from tiny images.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning multiple layers of features from tiny images

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Observation aeca4473-a686-4d82-ad91-29c92a4cb567 · outbound

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Exploring bidirectional bounds for minimax-training of Energy-based models Regularized autoencoders via relaxed injective probability flow

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.550787Z

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Observation 7b95dbbe-9712-477f-a404-2b0896ca0883 · outbound

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Exploring bidirectional bounds for minimax-training of Energy-based models Maximum Entropy Generators for Energy-Based Models

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Observation 200666db-9a2d-4d54-84f5-34a72628c29f · outbound

This paper cites A tutorial on energy-based learning.Predicting structured data, 1(0), 2006.

Exploring bidirectional bounds for minimax-training of Energy-based models A tutorial on energy-based learning.Predicting structured data, 1(0), 2006

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.540829Z

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Observation e0186ded-31fc-4d9a-a9f6-1bc3a96e69a8 · outbound

This paper cites Guiding energy-based models via contrastive latent variables.

Exploring bidirectional bounds for minimax-training of Energy-based models Guiding energy-based models via contrastive latent variables

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.530593Z

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Observation fc51f23e-7e7f-4e8c-b06a-a965f210f7da · outbound

This paper cites an unresolved cited work.

Exploring bidirectional bounds for minimax-training of Energy-based models Unresolved cited work

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Observation 2a61d55e-7ef6-4d5c-b556-3f395abb8d2c · outbound

This paper cites Deep learning face attributes in the wild.

Exploring bidirectional bounds for minimax-training of Energy-based models Deep learning face attributes in the wild

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Observation 6334dd3e-82d6-4adb-b255-64f1798b8e76 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

Exploring bidirectional bounds for minimax-training of Energy-based models Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

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Observation 87e3642d-8d59-42bf-ab98-c56e046bda7d · outbound

This paper cites Equation of state calculations by fast computing machines.The journal of chemical physics, 21(6):1087–1092, 1953.

Exploring bidirectional bounds for minimax-training of Energy-based models Equation of state calculations by fast computing machines.The journal of chemical physics, 21(6):1087–1092, 1953

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Observation d44514b0-6d83-43a6-bcdf-a9025ec4c549 · outbound

This paper cites Spectral nor- malization for generative adversarial networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Spectral nor- malization for generative adversarial networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.494112Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 397fb040-7a1f-4fbe-876f-579189dcfe6d · outbound

This paper cites MCMC using hamil- tonian dynamics.Handbook of markov chain monte carlo, 2(11):2, 2011.

Exploring bidirectional bounds for minimax-training of Energy-based models MCMC using hamil- tonian dynamics.Handbook of markov chain monte carlo, 2(11):2, 2011

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.484894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 462756be-8740-4fcf-b2b8-92f340a1c49b · outbound

This paper cites Learning non-convergent non-persistent short-run mcmc toward energy- based model.Advances in Neural Information Processing Systems, 32, 2019.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning non-convergent non-persistent short-run mcmc toward energy- based model.Advances in Neural Information Processing Systems, 32, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.475371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4190f880-bace-4895-8535-f5d9d68a86ca · outbound

This paper cites On the anatomy of MCMC-based maximum likeli- hood learning of energy-based models.

Exploring bidirectional bounds for minimax-training of Energy-based models On the anatomy of MCMC-based maximum likeli- hood learning of energy-based models

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.463750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 92c32820-82ff-4a8f-9515-8964a0f3720a · outbound

This paper cites Boltzmann machines and energy-based models.

Exploring bidirectional bounds for minimax-training of Energy-based models Boltzmann machines and energy-based models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:24.790475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8dc1bd6-a460-4b5d-9761-7c88da6371dd · outbound

This paper cites Automatic differ- entiation in pytorch.NIPS 2017 Workshop Autodiff, 2017.

Exploring bidirectional bounds for minimax-training of Energy-based models Automatic differ- entiation in pytorch.NIPS 2017 Workshop Autodiff, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.452096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 858d1f66-99d4-42a4-a122-0b8101d406d5 · outbound

This paper cites Unsupervised representation learning with deep convolutional generative adversar- ial networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Unsupervised representation learning with deep convolutional generative adversar- ial networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.439106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 72d0d9c9-4c1b-4e95-af4b-d561afd2720a · outbound

This paper cites Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan.

Exploring bidirectional bounds for minimax-training of Energy-based models Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.428423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 080b82b1-36fd-4b94-bee0-76fc6394f365 · outbound

This paper cites Assessing generative models via precision and recall.Advances in neural information processing systems, 31, 2018.

Exploring bidirectional bounds for minimax-training of Energy-based models Assessing generative models via precision and recall.Advances in neural information processing systems, 31, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.417669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea76d4d4-708a-444f-aa5f-85ad2de2fe21 · outbound

This paper cites Deep boltzmann machines.

Exploring bidirectional bounds for minimax-training of Energy-based models Deep boltzmann machines

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.406276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation abc8e5b7-0a71-4d85-abb1-863ddb5454c0 · outbound

This paper cites On the quantitative analysis of deep belief net- works.

Exploring bidirectional bounds for minimax-training of Energy-based models On the quantitative analysis of deep belief net- works

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.394502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e7135f81-ed90-4349-8144-06a367861de7 · outbound

This paper cites an unresolved cited work.

Exploring bidirectional bounds for minimax-training of Energy-based models Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-07T10:45:25.383809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc3f5abb-ea71-4c3f-884e-fefbd22f747b · outbound

This paper cites PhD the- sis, Universit´ e de Montr´ eal, Quebec, Canada, 2020.

Exploring bidirectional bounds for minimax-training of Energy-based models PhD the- sis, Universit´ e de Montr´ eal, Quebec, Canada, 2020

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40622f15-30f4-4b29-bec6-2febe3b89fa6 · outbound

This paper cites A spectral approach to gradient estimation for implicit distributions.

Exploring bidirectional bounds for minimax-training of Energy-based models A spectral approach to gradient estimation for implicit distributions

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.362857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6525eab3-5489-49e1-931e-8e7523ded083 · outbound

This paper cites Smolensky.Information Processing in Dynamical Systems: Foundations of Harmony Theory, page 194–281.

Exploring bidirectional bounds for minimax-training of Energy-based models Smolensky.Information Processing in Dynamical Systems: Foundations of Harmony Theory, page 194–281

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a4c5554d-8435-42ad-8a8c-5b9f9faba8c4 · outbound

This paper cites International conference on learning representations.

Exploring bidirectional bounds for minimax-training of Energy-based models International conference on learning representations

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:25.341580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3998ee63-8fdf-4ed7-b6a2-6faed43c175d · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.Advances in Neural Information Processing Systems, 32, 2019.

Exploring bidirectional bounds for minimax-training of Energy-based models Generative modeling by estimating gradients of the data distribution.Advances in Neural Information Processing Systems, 32, 2019

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:24.831863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23aa4079-5a98-4e65-977a-7ec9f411a4c5 · outbound

This paper cites Improved techniques for training score-based genera- tive models.Advances in neural information processing systems, 33:12438–12448, 2020.

Exploring bidirectional bounds for minimax-training of Energy-based models Improved techniques for training score-based genera- tive models.Advances in neural information processing systems, 33:12438–12448, 2020

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a6d127a-b91c-415a-96f9-b56d9d63c483 · outbound

This paper cites Maximum likelihood training of score-based diffusion models.Advances in Neural Information Processing Systems, 34: 1415–1428, 2021.

Exploring bidirectional bounds for minimax-training of Energy-based models Maximum likelihood training of score-based diffusion models.Advances in Neural Information Processing Systems, 34: 1415–1428, 2021

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f3c658a-a903-4a87-8537-e5f0a10ce2d3 · outbound

This paper cites Score-based generative mod- eling through stochastic differential equations.

Exploring bidirectional bounds for minimax-training of Energy-based models Score-based generative mod- eling through stochastic differential equations

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b8480b8-6126-4cbb-8d17-db8de84aa120 · outbound

This paper cites Consistency models.Pro- ceedings of the 40th International Conference on Machine Learning, 2023.

Exploring bidirectional bounds for minimax-training of Energy-based models Consistency models.Pro- ceedings of the 40th International Conference on Machine Learning, 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dc075db8-8cf2-452f-972d-8bd216c846d5 · outbound

This paper cites Cambridge Univer- sity Press, Cambridge, UK, 2019.

Exploring bidirectional bounds for minimax-training of Energy-based models Cambridge Univer- sity Press, Cambridge, UK, 2019

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 900f382d-d9a4-470b-904f-9bf4c03b86a9 · outbound

This paper cites Improving generalization and sta- bility of generative adversarial networks.

Exploring bidirectional bounds for minimax-training of Energy-based models Improving generalization and sta- bility of generative adversarial networks

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc2a279c-0868-4f8f-98b9-a1adfc5b4d02 · outbound

This paper cites A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011.

Exploring bidirectional bounds for minimax-training of Energy-based models A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011

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Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:24.854445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c30cbe7-a7f3-4a16-9b7a-71fdfc35e4dd · outbound

This paper cites The geom- etry of deep generative image models and its applications.

Exploring bidirectional bounds for minimax-training of Energy-based models The geom- etry of deep generative image models and its applications

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef7f05b2-38ff-4b88-9aa1-cb4f608b7e24 · outbound

This paper cites Sparse and deep gener- alizations of the frame model.Annals of Mathematical Sciences and Applications, 3(1): 211–254, 2018.

Exploring bidirectional bounds for minimax-training of Energy-based models Sparse and deep gener- alizations of the frame model.Annals of Mathematical Sciences and Applications, 3(1): 211–254, 2018

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 31d6a2b2-d36d-4ec3-8a6f-8909f24e7efc · outbound

This paper cites Tackling the generative learning trilemma with denoising diffusion gans.Inter- national Conference on Learning Representa- tions, 2022.

Exploring bidirectional bounds for minimax-training of Energy-based models Tackling the generative learning trilemma with denoising diffusion gans.Inter- national Conference on Learning Representa- tions, 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aa28ec2a-003c-4859-b07c-98d77b38e534 · outbound

This paper cites Learning sparse FRAME mod- els for natural image patterns.International Journal of Computer Vision, 114(2):91–112, 2015.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning sparse FRAME mod- els for natural image patterns.International Journal of Computer Vision, 114(2):91–112, 2015

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77193ca0-7c69-42a7-89fe-361df3b9a0ea · outbound

This paper cites Inducing wavelets into random fields via generative boosting.Applied and Computational Harmonic Analysis, 41(1):4– 25, 2016.

Exploring bidirectional bounds for minimax-training of Energy-based models Inducing wavelets into random fields via generative boosting.Applied and Computational Harmonic Analysis, 41(1):4– 25, 2016

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8bb2a587-6a5f-41d0-b746-b1e83fe0d08b · outbound

This paper cites Synthesizing dynamic patterns by spatial-temporal generative convnet.

Exploring bidirectional bounds for minimax-training of Energy-based models Synthesizing dynamic patterns by spatial-temporal generative convnet

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e44fa9e2-f88b-4176-aeca-22af6b0cfc06 · outbound

This paper cites Learning descriptor networks for 3d shape synthesis and analysis.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning descriptor networks for 3d shape synthesis and analysis

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12b5fe30-94a4-4ed2-bdc5-ae5c2f54af99 · outbound

This paper cites Cooperative learning of energy-based model and latent variable model via MCMC teaching.

Exploring bidirectional bounds for minimax-training of Energy-based models Cooperative learning of energy-based model and latent variable model via MCMC teaching

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3b06609e-98fd-423b-a883-3f1e9e5e4396 · outbound

This paper cites Cooperative training of descriptor and generator networks.IEEE transactions on pattern analysis and machine intelligence, 42(1):27–45, 2018b.

Exploring bidirectional bounds for minimax-training of Energy-based models Cooperative training of descriptor and generator networks.IEEE transactions on pattern analysis and machine intelligence, 42(1):27–45, 2018b

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 58126242-653b-4f01-99a9-f90202cc74fd · outbound

This paper cites Cooper- ative training of fast thinking initializer and slow thinking solver for conditional learning.

Exploring bidirectional bounds for minimax-training of Energy-based models Cooper- ative training of fast thinking initializer and slow thinking solver for conditional learning

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c940ef52-bca3-4e86-bb45-04fc5a8eea05 · outbound

This paper cites Learning energy-based model with variational auto-encoder as amortized sampler.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning energy-based model with variational auto-encoder as amortized sampler

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 791957fe-c66b-4335-be96-4f2b0db02347 · outbound

This paper cites Generative Adversarial Networks as Variational Training of Energy Based Models.

Exploring bidirectional bounds for minimax-training of Energy-based models Generative Adversarial Networks as Variational Training of Energy Based Models

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b94334e7-4c71-40dc-8e03-731dc38b72bc · outbound

This paper cites Grade: Gibbs reaction and diffusion equations.

Exploring bidirectional bounds for minimax-training of Energy-based models Grade: Gibbs reaction and diffusion equations

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 25b613e0-a5a8-4952-9ece-907592d1e570 · outbound

This paper cites Filters, random fields and maxi- mum entropy (FRAME): Towards a unified theory for texture modeling.International Journal of Computer Vision, 27(2):107–126, 1998.

Exploring bidirectional bounds for minimax-training of Energy-based models Filters, random fields and maxi- mum entropy (FRAME): Towards a unified theory for texture modeling.International Journal of Computer Vision, 27(2):107–126, 1998

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c19cdf0-80f8-411f-9c67-19b8f43c9367 · outbound

This paper cites Learning energy-based models by cooperative diffusion recovery likelihood.

Exploring bidirectional bounds for minimax-training of Energy-based models Learning energy-based models by cooperative diffusion recovery likelihood

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Source-reported events for the cited work

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