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Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
K. Arora, L. El Asri, H. Bahuleyan, and J. Chi Kit Cheung. Association for Computational Linguistics (ACL), 2022
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Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Y. Gong, H. Hajimirsadeghi, J. He, T. Durand, and G. Mori. International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
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Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
V. Balasubramanian, I. Kobyzev, H. Bahuleyan, and O. Vechtomova. Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021
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Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
J. Yu, K. Derpanis, and M. Brubaker. Conference on Neural Information Processing Systems (NeurIPS), 2020
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Evaluating Lossy Compression Rates of Deep Generative Models
Evaluating Lossy Compression Rates of Deep Generative Models
*S. Huang, *A. Makhzani, Y. Cao, and R. Grosse. International Conference on Machine Learning (ICML), 2020
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Normalizing Flows: An Introduction and Review of Current Methods
Normalizing Flows: An Introduction and Review of Current Methods
I. Kobyzev, S. Prince, and M. Brubaker. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
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On Minimax Optimality of GANs for Robust Mean Estimation
On Minimax Optimality of GANs for Robust Mean Estimation
G. W. Ding, R. Huang, Y. Yu, and K. Wu. International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
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Variational Selective Autoencoder
Variational Selective Autoencoder
*Y. Gong, *H. Hajimirsadeghi, *J. He, *M. Nawhal, T. Durand, and *G. Mori. Advances in Approximate Bayesian Inference (AABI), 2019
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Arbitrarily-conditioned Data Imputation
Arbitrarily-conditioned Data Imputation
M. Carvalho, T. Durand, J. He, N. Mehrasa, and G. Mori. Advances in Approximate Bayesian Inference (AABI), 2019
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Noise Flow: Noise Modeling with Conditional Normalizing Flows
Noise Flow: Noise Modeling with Conditional Normalizing Flows
A. Abdelhamed, M. Brubaker, and M. S. Brown. International Conference on Computer Vision (ICCV), 2019
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Lifelong GAN: Continual Learning for Conditional Image Generation
Lifelong GAN: Continual Learning for Conditional Image Generation
*M. Zhai, *L. Chen, F. Tung, J. He, M. Nawhal, and G. Mori. International Conference on Computer Vision (ICCV), 2019
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LayoutVAE: Stochastic Scene Layout Generation from a Label Set
LayoutVAE: Stochastic Scene Layout Generation from a Label Set
A. Jyothi, T. Durand, J. He, L. Sigal, and G. Mori. International Conference on Computer Vision (ICCV), 2019
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