Deep Learning with Gaussian Proc

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,

Amund Tveit

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 Muñoz 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

2015Gaussian discrete restricted Boltzmann machine: theory and its applications: a thesis presented in partial fulfilment of the requirements for the degree of Master of …S Manoharan

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

2014Non-negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect RecognitionJ Glass

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

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https://amundtveit.com/2016/12/02/deep-learning-with-gaussian-process/