Variational Autoencoders Pursue PCA Directions (by Accident)
暂无分享,去创建一个
[1] Joelle Pineau,et al. A Deep Reinforcement Learning Chatbot , 2017, ArXiv.
[2] Hedvig Kjellström,et al. Advances in Variational Inference , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[4] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[5] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[6] Roland Vollgraf,et al. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms , 2017, ArXiv.
[7] M. Turk,et al. Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.
[8] H. Bourlard,et al. Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.
[9] Jürgen Schmidhuber,et al. Learning Factorial Codes by Predictability Minimization , 1992, Neural Computation.
[10] Roger B. Grosse,et al. Isolating Sources of Disentanglement in Variational Autoencoders , 2018, NeurIPS.
[11] Yoshua Bengio,et al. Disentangling Factors of Variation via Generative Entangling , 2012, ArXiv.
[12] Bernhard Schölkopf,et al. Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations , 2018, ICML.
[13] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[14] Jordi Bonada,et al. Modeling and Transforming Speech Using Variational Autoencoders , 2016, INTERSPEECH.
[15] David P. Wipf,et al. Diagnosing and Enhancing VAE Models , 2019, ICLR.
[16] Andriy Mnih,et al. Disentangling by Factorising , 2018, ICML.
[17] Jan Peters,et al. Stable reinforcement learning with autoencoders for tactile and visual data , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[18] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[19] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Danica Kragic,et al. Deep predictive policy training using reinforcement learning , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[21] Bernhard Schölkopf,et al. From Variational to Deterministic Autoencoders , 2019, ICLR.
[22] Alexander A. Alemi,et al. Fixing a Broken ELBO , 2017, ICML.
[23] LinLin Shen,et al. Deep Feature Consistent Variational Autoencoder , 2016, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[24] Philipp Birken,et al. Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.
[25] Sebastian Nowozin,et al. Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks , 2017, ICML.
[26] Christopher Burgess,et al. DARLA: Improving Zero-Shot Transfer in Reinforcement Learning , 2017, ICML.
[27] Sebastian Nowozin,et al. ISA-VAE: Independent Subspace Analysis with Variational Autoencoders , 2018 .
[28] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[29] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[30] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[31] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[32] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[33] Sergey Levine,et al. Visual Reinforcement Learning with Imagined Goals , 2018, NeurIPS.
[34] B. AfeArd. CALCULATING THE SINGULAR VALUES AND PSEUDOINVERSE OF A MATRIX , 2022 .
[35] Daan Wierstra,et al. Towards Conceptual Compression , 2016, NIPS.
[36] Guillaume Desjardins,et al. Understanding disentangling in β-VAE , 2018, ArXiv.
[37] Daan Wierstra,et al. One-Shot Generalization in Deep Generative Models , 2016, ICML.
[38] Navdeep Jaitly,et al. Adversarial Autoencoders , 2015, ArXiv.
[39] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[40] Yee Whye Teh,et al. Disentangling Disentanglement in Variational Autoencoders , 2018, ICML.
[41] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[42] Zhiting Hu,et al. Improved Variational Autoencoders for Text Modeling using Dilated Convolutions , 2017, ICML.
[43] Karl Ridgeway,et al. A Survey of Inductive Biases for Factorial Representation-Learning , 2016, ArXiv.
[44] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[45] Bin Dai,et al. Hidden Talents of the Variational Autoencoder. , 2017 .
[46] Gene H. Golub,et al. Calculating the singular values and pseudo-inverse of a matrix , 2007, Milestones in Matrix Computation.