When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
[หนังสือ][B] Applied stochastic differential equations
Stochastic differential equations are differential equations whose solutions are stochastic
processes. They exhibit appealing mathematical properties that are useful in modeling …
processes. They exhibit appealing mathematical properties that are useful in modeling …
Edge: Explaining deep reinforcement learning policies
With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …
increasing need to understand and interpret DRL policies. While recent research has …
A flexible state–space model for learning nonlinear dynamical systems
We consider a nonlinear state–space model with the state transition and observation
functions expressed as basis function expansions. The coefficients in the basis function …
functions expressed as basis function expansions. The coefficients in the basis function …
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the system state forward
in time are estimated. However, for many complex systems it is practically impossible to …
in time are estimated. However, for many complex systems it is practically impossible to …
A stochastic variational framework for recurrent Gaussian processes models
Abstract Gaussian Processes (GPs) models have been successfully applied to the problem
of learning from sequential observations. In such context, the family of Recurrent Gaussian …
of learning from sequential observations. In such context, the family of Recurrent Gaussian …
Free-form variational inference for Gaussian process state-space models
Gaussian process state-space models (GPSSMs) provide a principled and flexible approach
to modeling the dynamics of a latent state, which is observed at discrete-time points via a …
to modeling the dynamics of a latent state, which is observed at discrete-time points via a …
Sequential estimation of Gaussian process-based deep state-space models
We consider the problem of sequential estimation of the unknowns of state-space and deep
state-space models that include estimation of functions and latent processes of the models …
state-space models that include estimation of functions and latent processes of the models …
Neural dynamics discovery via gaussian process recurrent neural networks
Latent dynamics discovery is challenging in extracting complex dynamics from
highdimensional noisy neural data. Many dimensionality reduction methods have been …
highdimensional noisy neural data. Many dimensionality reduction methods have been …
Bayesian online change point detection with Hilbert space approximate Student-t process
J Sellier, P Dellaportas - International Conference on …, 2023 - proceedings.mlr.press
In this paper, we introduce a variant of Bayesian online change point detection with a
reducedrank Student-t process (TP) and dependent Student-t noise, as a nonparametric …
reducedrank Student-t process (TP) and dependent Student-t noise, as a nonparametric …