暂无分享,去创建一个
Tong Che | Yoshua Bengio | Hugo Larochelle | Jascha Sohl-Dickstein | Yuan Cao | Ruixiang Zhang | Liam Paull | Yoshua Bengio | H. Larochelle | J. Sohl-Dickstein | Tong Che | Ruixiang Zhang | Yuan Cao | L. Paull
[1] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[2] G. Casella,et al. Generalized Accept-Reject sampling schemes , 2004 .
[3] David J. C. MacKay,et al. Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.
[4] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[5] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[6] Fu Jie Huang,et al. A Tutorial on Energy-Based Learning , 2006 .
[7] Tijmen Tieleman,et al. Training restricted Boltzmann machines using approximations to the likelihood gradient , 2008, ICML '08.
[8] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[9] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[10] Jascha Sohl-Dickstein,et al. A new method for parameter estimation in probabilistic models: Minimum probability flow , 2011, Physical review letters.
[11] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[12] Yoshua Bengio,et al. Better Mixing via Deep Representations , 2012, ICML.
[13] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[14] Gilles Louppe,et al. Approximating Likelihood Ratios with Calibrated Discriminative Classifiers , 2015, 1506.02169.
[15] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[16] Yann LeCun,et al. Energy-based Generative Adversarial Network , 2016, ICLR.
[17] Stefano Ermon,et al. Generative Adversarial Imitation Learning , 2016, NIPS.
[18] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[19] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[20] Sergey Levine,et al. A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models , 2016, ArXiv.
[21] Yoshua Bengio,et al. Deep Directed Generative Models with Energy-Based Probability Estimation , 2016, ArXiv.
[22] Alexei A. Efros,et al. Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.
[23] Yoshua Bengio,et al. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[25] Fan Yang,et al. Good Semi-supervised Learning That Requires a Bad GAN , 2017, NIPS.
[26] David Pfau,et al. Unrolled Generative Adversarial Networks , 2016, ICLR.
[27] Sebastian Nowozin,et al. Stabilizing Training of Generative Adversarial Networks through Regularization , 2017, NIPS.
[28] Karen Simonyan,et al. Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders , 2017, ICML.
[29] Tian Han,et al. Alternating Back-Propagation for Generator Network , 2016, AAAI.
[30] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[31] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[32] Ping Tan,et al. DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[33] Yoshua Bengio,et al. Mode Regularized Generative Adversarial Networks , 2016, ICLR.
[34] James Zou,et al. AI can be sexist and racist — it’s time to make it fair , 2018, Nature.
[35] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[36] Rémi Munos,et al. Autoregressive Quantile Networks for Generative Modeling , 2018, ICML.
[37] Oriol Vinyals,et al. Learning Implicit Generative Models with the Method of Learned Moments , 2018, ICML.
[38] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[39] Yang Lu,et al. Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching , 2018, AAAI.
[40] Yang Lu,et al. Learning Generative ConvNets via Multi-grid Modeling and Sampling , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[41] Mario Lucic,et al. Are GANs Created Equal? A Large-Scale Study , 2017, NeurIPS.
[42] Stefano Ermon,et al. Variational Rejection Sampling , 2018, AISTATS.
[43] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[44] Yoshua Bengio,et al. Maximum Entropy Generators for Energy-Based Models , 2019, ArXiv.
[45] Joshua V. Dillon,et al. NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport , 2019, 1903.03704.
[46] Andriy Mnih,et al. Resampled Priors for Variational Autoencoders , 2018, AISTATS.
[47] Akinori Tanaka,et al. Discriminator optimal transport , 2019, NeurIPS.
[48] J. Hobson. Enlightenment Now: The Case for Reason, Science, Humanism, and Progress , 2019, Occupational Medicine.
[49] Trevor Darrell,et al. Discriminator Rejection Sampling , 2018, ICLR.
[50] Yan Wu,et al. LOGAN: Latent Optimisation for Generative Adversarial Networks , 2019, ArXiv.
[51] Erik Nijkamp,et al. Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model , 2019, NeurIPS.
[52] Yang Song,et al. Generative Modeling by Estimating Gradients of the Data Distribution , 2019, NeurIPS.
[53] Igor Mordatch,et al. Implicit Generation and Modeling with Energy Based Models , 2019, NeurIPS.
[54] Le Song,et al. Exponential Family Estimation via Adversarial Dynamics Embedding , 2019, NeurIPS.
[55] Tian Han,et al. Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Eric Horvitz,et al. Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting , 2019, DGS@ICLR.
[57] Jianfeng Feng,et al. On Fenchel Mini-Max Learning , 2019, NeurIPS.
[58] Carlos Guestrin,et al. Adversarial Fisher Vectors for Unsupervised Representation Learning , 2019, NeurIPS.
[59] G. Tucker,et al. Energy-Inspired Models: Learning with Sampler-Induced Distributions , 2019, NeurIPS.
[60] Jason Yosinski,et al. Metropolis-Hastings Generative Adversarial Networks , 2018, ICML.
[61] Tero Karras,et al. Analyzing and Improving the Image Quality of StyleGAN , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[62] Anna Dai. Generative Modeling , 2020 .
[63] Andrew M. Dai,et al. Flow Contrastive Estimation of Energy-Based Models , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Arthur Gretton,et al. KALE: When Energy-Based Learning Meets Adversarial Training , 2020, ArXiv.
[65] Tian Han,et al. Joint Training of Variational Auto-Encoder and Latent Energy-Based Model , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[66] Myle Ott,et al. Residual Energy-Based Models for Text Generation , 2020, ICLR.
[67] J. B. King. Enlightenment Now: The Case for Reason, Science, Humanism, and Progress , 2020, Theology and Science.
[68] Tian Han,et al. Learning Latent Space Energy-Based Prior Model , 2020, NeurIPS.
[69] Mohammad Norouzi,et al. Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One , 2019, ICLR.