Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

Alemi, A., Fischer, I., Dillon, J., & Murphy, K. (2017). Deep variational information bottleneck. In 5th international conference on learning representations, ICLR. Bishop, 2006 Bouchacourt, D., Tomioka, R., & Nowozin, S. (2018). Multi-level variational autoencoder: Learning disentangled representations from grouped observations. In Thirty-second AAAI conference on artificial intelligence. Bui, 2019, Projection-free kernel principal component analysis for denoising, Neurocomputing, 10.1016/j.neucom.2019.04.042 Burgess, 2018 Chen, 2017, Multi-view generative adversarial networks, 175 Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems (pp. 2172–2180). Chen, 2018, Isolating sources of disentanglement in variational autoencoders, 2610 Dumoulin, 2016 Eastwood, C., & Williams, C. K. I. (2018). A framework for the quantitative evaluation of disentangled representations. In International conference on learning representations. https://openreview.net/forum?id=By-7dz-AZ. Florensa, 2018, Automatic goal generation for reinforcement learning agents, Vol. 80, 1515 Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems 27: annual conference on neural information processing systems 2014 (pp. 2672–2680). Hastie, 2001 Heusel, 2017, GANS trained by a two time-scale update rule converge to a local Nash equilibrium, 6629 Higgins, 2017, Beta-VAE: Learning basic visual concepts with a constrained variational framework., 5th international conference on learning representations, ICLR, 2, 6 Honeine, 2011, Preimage problem in kernel-based machine learning, IEEE Signal Processing Magazine, 28, 77, 10.1109/MSP.2010.939747 Houthuys, L., & Suykens, J. A. K. (2018). Tensor learning in multi-view kernel PCA. In 27th international conference on artificial neural networks ICANN: Vol. 11140 (pp. 205–215). Kingma, 2014 Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. In 2nd international conference on learning representations, ICLR 2014, Banff, AB, Canada, April 14–16, 2014, conference track proceedings. Krizhevsky, 2009 Kwok, 2003, The pre-image problem in kernel methods, IEEE Transactions on Neural Networks, 15, 1517, 10.1109/TNN.2004.837781 Larochelle, 2008, Classification using discriminative restricted Boltzmann machines, 536 Lawrence, 2005, Probabilistic non-linear principal component analysis with Gaussian process latent variable models, JMLR, 6, 1783 LeCun, 2010 LeCun, Y., Huang, F. J., & Bottou, L. (2004). Learning methods for generic object recognition with invariance to pose and lighting. In Computer vision and pattern recognition, 2004: Vol. 2 (pp. II–97–104). Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of international conference on computer vision. Liu, 2016, Coupled generative adversarial networks, 469 Matthey, 2017 McInnes, 2018, UMAP: Uniform manifold approximation and projection, Journal of Open Source Software, 3, 861, 10.21105/joss.00861 Mercer, 1909, Functions of positive and negative type, and their connection the theory of integral equations, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 209, 415 Mika, 1999, Kernel PCA and de-noising in feature spaces, 536 Pu, 2016, Variational autoencoder for deep learning of images, labels and captions, 2360 Rabiner, 1986, An introduction to hidden Markov models, IEEE ASSP Magazine, 3, 4, 10.1109/MASSP.1986.1165342 Ridgeway, 2016, A survey of inductive biases for factorial representation-learning, CoRR, abs/1612.05299 Rockafellar, 1974 Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann machines. In Proceedings of the 12th international conference on artificial intelligence and statistics Volume 5 of JMLR. Salakhutdinov, 2007, Restricted Boltzmann machines for collaborative filtering, 791 Sangkloy, 2016, The sketchy database: Learning to retrieve badly drawn bunnies, ACM Transactions on Graphics (proceedings of SIGGRAPH), 10.1145/2897824.2925954 Sangkloy, 2016, The sketchy database: learning to retrieve badly drawn bunnies, ACM Transactions on Graphics, 35, 1, 10.1145/2897824.2925954 Schmidhuber, 1992, Learning factorial codes by predictability minimization, Neural Computation, 4, 863, 10.1162/neco.1992.4.6.863 Scholkopf, 2001 Schreurs, J., & Suykens, J. A. K. (2018). Generative Kernel PCA. In European symposium on artificial neural networks, computational intelligence and machine learning (pp. 129–134). Smolensky, 1986, 194 Srivastava, 2012, Multimodal learning with deep boltzmann machines, 2222 Suykens, 2017, Deep restricted kernel machines using conjugate feature duality, Neural Computation, 29, 2123, 10.1162/neco_a_00984 Suykens, 2002 Suykens, 2003, A support vector machine formulation to PCA analysis and its kernel version, IEEE Transactions on Neural Networks, 14, 447, 10.1109/TNN.2003.809414 Suzuki, 2016 Tipping, 1999, Probabilistic principal component analysis, Journal Of The Royal Statistical Society, series B, 61, 611, 10.1111/1467-9868.00196 Tran, L., Yin, X., & Liu, X. (2017). Disentangled representation learning GAN for pose-invariant face recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1415–1424). Van Den Oord, 2016, Pixel recurrent neural networks, 1747 Vincent, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of Machine Learning Research, 11, 3371 Weston, J., Schölkopf, B., & Bakir, G. H. (2004). Learning to find pre-images. In NIPS 16 (pp. 449–456). Wu, 2018, Multimodal generative models for scalable weakly-supervised learning, 5575 Xiao, 2017 Yeh, R. A., Chen, C., Yian Lim, T., Schwing, A. G., Hasegawa-Johnson, M., & Do, M. N. (2017). Semantic image inpainting with deep generative models. In The IEEE conference on computer vision and pattern recognition.