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Gaussian Processes

568 directly classified papers

Papers per year

Papers

Targeting EEG/LFP Synchrony with Neural Nets NIPS 2017
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes NIPS 2017
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models NIPS 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes NIPS 2017
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks NIPS 2017
GP CaKe: Effective brain connectivity with causal kernels NIPS 2017
Gaussian process based nonlinear latent structure discovery in multivariate spike train data NIPS 2017
Non-Stationary Spectral Kernels NIPS 2017
Streaming Sparse Gaussian Process Approximations NIPS 2017
Variational Inference for Gaussian Process Models with Linear Complexity NIPS 2017
Student-t Process Regression with Student-t Likelihood IJCAI 2017
Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression IJCAI 2017
SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier SEMEVAL 2017
DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output SEMEVAL 2017
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control ICML 2017
Cross-Spectral Factor Analysis NIPS 2017
Modelling Representation Noise in Emotion Analysis using Gaussian Processes IJCNLP 2017
Variational Bayesian Multiple Instance Learning With Gaussian Processes CVPR 2017
Extending Model-based Policy Gradients for Robots in Heteroscedastic Environments CORL 2017
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation JMLR 2017
Learning Scalable Deep Kernels with Recurrent Structure JMLR 2017
Bayesian Learning of Dynamic Multilayer Networks JMLR 2017
Active Incremental Learning of Robot Movement Primitives CORL 2017
Gray-box Inference for Structured Gaussian Process Models AISTATS 2017
A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing IJCAI 2017