Deep Learning with Gaussian Process

Gaussian Process is a statistical model where observations are in the continuous domain, to learn more check out a tutorial on gaussian process (by Univ.of Cambridge’s Zoubin G.). Gaussian Process is an infinite-dimensional generalization of multivariate 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

Year  Title Author
2016   Inverse Reinforcement Learning via Deep Gaussian Process  M Jin, C Spanos
2016   Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM  CL Li, S Ravanbakhsh, B Poczos
2016   Large Scale Gaussian Process for Overlap-based Object Proposal Scoring  SL Pintea, S Karaoglu, JC van Gemert
2016   Gaussian Neuron in Deep Belief Network for Sentiment Prediction  Y Jin, D Du, H Zhang
2016   Fast, Exact and Multi-Scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs  S Chandra, I Kokkinos
2016   The Variational Gaussian Process  D Tran, R Ranganath, DM Blei
2016   Probabilistic Feature Learning Using Gaussian Process Auto-Encoders  S Olofsson
2016   Sequential Inference for Deep Gaussian Process  Y Wang, M Brubaker, B Chaib
2016   Gaussian Copula Variational Autoencoders for Mixed Data  S Suh, S Choi
2016   Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising  K Zhang, W Zuo, Y Chen, D Meng, L Zhang
2016   Image super-resolution using non-local Gaussian process regression  H Wang, X Gao, K Zhang, J Li
2016   Gaussian Conditional Random Field Network for Semantic Segmentation  R Vemulapalli, O Tuzel, MY Liu, R Chellappa
2016   Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors  C Louizos, M Welling
2016   Deep Gaussian Processes for Regression using Approximate Expectation Propagation  TD Bui, D Hernández
2015   Learning to Assess Terrain from Human Demonstration Using an Introspective Gaussian Process Classifier  LP Berczi, I Posner, TD Barfoot
2015   Assessing the Degree of Nativeness and Parkinson’s Condition Using Gaussian Processes and Deep Rectifier Neural Networks  T Grósz, R Busa
2015   Gaussian processes methods for nostationary regression  L Muñoz González
2015   Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?  R Giryes, G Sapiro, AM Bronstein
2015   Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes  H Yu, F Khan, V Garaniya
2015   Interactions Between Gaussian Processes and Bayesian Estimation  YL Wang
2015   Gaussian 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
2015   Prosody Generation Using Frame-based Gaussian Process Regression  T Koriyama, T Kobayashi
2015   Mean-Field Inference in Gaussian Restricted Boltzmann Machine  C Takahashi, M Yasuda
2015   Variational Auto-encoded Deep Gaussian Processes  Z Dai, A Damianou, J González, N Lawrence
2015   Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation  TD Bui, JM Hernández
2015   Accurate Object Detection and Semantic Segmentation using Gaussian Mixture Model and CNN  S Jain, S Dehriya, YK Jain
2014   Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge  J Wang, C Kang, Y He, S Xiang, C Pan
2014   Non-negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition  J Glass
2014   Gaussian Process Models with Parallelization and GPU acceleration  Z Dai, A Damianou, J Hensman, N Lawrence
2014   Parametric Speech Synthesis Using Local and Global Sparse Gaussian  T Koriyama, T Nose, T Kobayashi
2014   On the Link Between Gaussian Homotopy Continuation and Convex Envelopes  H Mobahi, JW Fisher III
2014   Improving Deep Neural Networks Using State Projection Vectors Of Subspace Gaussian Mixture Model As Features  M Karthick, S Umesh
2014   A Theoretical Analysis of Optimization by Gaussian Continuation  H Mobahi, JW Fisher III
2014   Factoring Variations in Natural Images with Deep Gaussian Mixture Models  A van den Oord, B Schrauwen
2014   Feature representation with Deep Gaussian processes

You may also like

1 Comment

Leave a Reply

Your email address will not be published. Required fields are marked *