Gaussian Processis a statistical model where observations are in the continuous domain, to learn more check outa tutorial on gaussian process(by Univ.of Cambridge’sZoubin G.). Gaussian Process is an infinite-dimensional generalization ofmultivariate normal distributions.
Researchers from University of Sheffield – Andreas C. Damanianou and Neil D. Lawrence –started using Gaussian Process with Deep Belief Networks (in 2013). This Blog post contains recent papers related to combining Deep Learning with Gaussian Process.
Best regards,
YEARTITLEAUTHOR
2016Inverse Reinforcement Learning via Deep Gaussian ProcessM Jin, C Spanos
2016Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBMCL Li, S Ravanbakhsh, B Poczos
2016Large Scale Gaussian Process for Overlap-based Object Proposal ScoringSL Pintea, S Karaoglu, JC van Gemert
2016Gaussian Neuron in Deep Belief Network for Sentiment PredictionY Jin, D Du, H Zhang
2016Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFsS Chandra, I Kokkinos
2016The Variational Gaussian ProcessD Tran, R Ranganath, DM Blei
2016Probabilistic Feature Learning Using Gaussian Process Auto-EncodersS Olofsson
2016Sequential Inference for Deep Gaussian ProcessY Wang, M Brubaker, B Chaib
2016Gaussian Copula Variational Autoencoders for Mixed DataS Suh, S Choi
2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingK Zhang, W Zuo, Y Chen, D Meng, L Zhang
2016Image super-resolution using non-local Gaussian process regressionH Wang, X Gao, K Zhang, J Li
2016Gaussian Conditional Random Field Network for Semantic SegmentationR Vemulapalli, O Tuzel, MY Liu, R Chellappa
2016Structured and Efficient Variational Deep Learning with Matrix Gaussian PosteriorsC Louizos, M Welling
2016Deep Gaussian Processes for Regression using Approximate Expectation PropagationTD Bui, D Hernández
2015Learning to Assess Terrain from Human Demonstration Using an Introspective Gaussian Process ClassifierLP Berczi, I Posner, TD Barfoot
2015Assessing the Degree of Nativeness and Parkinson’s Condition Using Gaussian Processes and Deep Rectifier Neural NetworksT Grósz, R Busa
2015Gaussian processes methods for nostationary regressionL Mu?oz González
2015Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?R Giryes, G Sapiro, AM Bronstein
2015Nonlinear Gaussian Belief Network based fault diagnosis for industrial processesH Yu, F Khan, V Garaniya
2015Interactions Between Gaussian Processes and Bayesian EstimationYL Wang
2015Prosody Generation Using Frame-based Gaussian Process RegressionT Koriyama, T Kobayashi
2015Mean-Field Inference in Gaussian Restricted Boltzmann MachineC Takahashi, M Yasuda
2015Variational Auto-encoded Deep Gaussian ProcessesZ Dai, A Damianou, J González, N Lawrence
2015Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic BackpropagationTD Bui, JM Hernández
2015Accurate Object Detection and Semantic Segmentation using Gaussian Mixture Model and CNNS Jain, S Dehriya, YK Jain
2014Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing ChallengeJ Wang, C Kang, Y He, S Xiang, C Pan
2014Gaussian Process Models with Parallelization and GPU accelerationZ Dai, A Damianou, J Hensman, N Lawrence
2014Parametric Speech Synthesis Using Local and Global Sparse GaussianT Koriyama, T Nose, T Kobayashi
2014On the Link Between Gaussian Homotopy Continuation and Convex EnvelopesH Mobahi, JW Fisher III
2014Improving Deep Neural Networks Using State Projection Vectors Of Subspace Gaussian Mixture Model As FeaturesM Karthick, S Umesh
2014A Theoretical Analysis of Optimization by Gaussian ContinuationH Mobahi, JW Fisher III
2014Factoring Variations in Natural Images with Deep Gaussian Mixture ModelsA van den Oord, B Schrauwen
2014Feature representation with Deep Gaussian processes
AIArtificial Intelligencedeep learningGaussian Process
引用網(wǎng)址
https://amundtveit.com/2016/12/02/deep-learning-with-gaussian-process/
