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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 Abstract—Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have the problems of high complexity. In this paper, we firstly propose a novel behaviorExpand
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A survey of kernel and spectral methods for clustering
Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interestExpand
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ODE parameter inference using adaptive gradient matching with Gaussian processes
Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical yet computationally chal- lenging problem, due to the need to fol- low each parameterExpand
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MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently whenExpand
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Random Feature Expansions for Deep Gaussian Processes
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with soundExpand
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Monte Carlo Strength Evaluation: Fast and Reliable Password Checking
Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices andExpand
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Preconditioning Kernel Matrices
The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use theExpand
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Pseudo-Marginal Bayesian Inference for Gaussian Processes
  • M. Filippone, M. Girolami
  • Mathematics, Computer Science
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2 October 2013
The main challenges that arise when adopting Gaussian process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parametersExpand
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Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes
Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach that detects common conversational socialExpand
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A comparative evaluation of stochastic-based inference methods for Gaussian process models
Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling capabilities and interpretability. The fully Bayesian treatment of GP models is analyticallyExpand
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