暂无分享,去创建一个
Peter Dayan | Zoubin Ghahramani | Theofanis Karaletsos | P. Dayan | Zoubin Ghahramani | Theofanis Karaletsos
[1] Margrit Betke,et al. Hierarchical Bayesian Neural Networks for Personalized Classification , 2016 .
[2] Kenneth O. Stanley,et al. On the Performance of Indirect Encoding Across the Continuum of Regularity , 2011, IEEE Transactions on Evolutionary Computation.
[3] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[4] Daan Wierstra,et al. Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models , 2014, ArXiv.
[5] D. Mackay,et al. Bayesian neural networks and density networks , 1995 .
[6] Yura N. Perov,et al. Learning Probabilistic Programs , 2014, ArXiv.
[7] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[8] Ben Glocker,et al. Implicit Weight Uncertainty in Neural Networks. , 2017 .
[9] M. West. On scale mixtures of normal distributions , 1987 .
[10] Neil D. Lawrence,et al. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.
[11] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[12] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[13] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.
[14] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[15] Sebastian Risi,et al. An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons , 2012, Artificial Life.
[16] Kenneth O. Stanley,et al. Compositional Pattern Producing Networks : A Novel Abstraction of Development , 2007 .
[17] Noah D. Goodman,et al. Learning a theory of causality. , 2011, Psychological review.
[18] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[19] Neil D. Lawrence,et al. The Emergence of Organizing Structure in Conceptual Representation , 2018, Cognitive science.
[20] Diederik P. Kingma,et al. Stochastic Gradient VB and the Variational Auto-Encoder , 2013 .
[21] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[22] Julien Cornebise,et al. Weight Uncertainty in Neural Networks , 2015, ArXiv.
[23] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[24] Michael I. Jordan,et al. Learning Programs: A Hierarchical Bayesian Approach , 2010, ICML.
[25] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[26] Miguel Lázaro-Gredilla,et al. Local Expectation Gradients for Black Box Variational Inference , 2015, NIPS.
[27] Max Welling,et al. Multiplicative Normalizing Flows for Variational Bayesian Neural Networks , 2017, ICML.
[28] Kenneth O. Stanley,et al. Autonomous Evolution of Topographic Regularities in Artificial Neural Networks , 2010, Neural Computation.
[29] Christopher D. Manning,et al. Hierarchical Bayesian Domain Adaptation , 2009, NAACL.
[30] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[31] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[32] Max Welling,et al. Variational Dropout and the Local Reparameterization Trick , 2015, NIPS 2015.
[33] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[34] Neil D. Lawrence,et al. Bayesian Gaussian Process Latent Variable Model , 2010, AISTATS.
[35] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[36] Ryan P. Adams,et al. Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks , 2015, ICML.