Approximate Bayesian Inference Team Seminar (Talk by Jung Yohan, Korea Advanced Institute of Science & Technology,Information & Electronics Research).

Tue, 21 Nov 2023 14:00 - 15:00 JST
Online Link visible to participants

Registration is closed

Get invited to future events

Free admission


This is an online seminar.
Registration is required.

【Speaker】Jung Yohan
Korea Advanced Institute of Science & Technology,Information & Electronics Research

Approximate Bayesian Inference for Stationary Priors and Its Application with Neural Processes

Stationarity, the assumption that the statistical properties of a dataset remain constant over time, is a widely-used inductive bias in modeling datasets. Gaussian Processes (GPs) have frequently been employed to model this stationarity, utilizing a stationary kernel function for the covariance structure. However, when dealing with a given dataset, selecting an appropriate kernel function from the extensive class of stationary kernels poses a challenge.

In this talk, I will first introduce the construction of a flexible kernel function capable of modeling any stationary process based on Bochner’s theorem. Subsequently, I will introduce the proposed approximate Bayesian inference method designed to train the parameters of this flexible kernel function. Next, I will discuss how deep neural networks (DNNs), referred to as the Neural Process (NP), can be used to model stationary processes. I will present the Bayesian extension of the NP, leveraging task-dependent stationary priors to enhance the modeling of stationary processes.

About this community



Public events of RIKEN Center for Advanced Intelligence Project (AIP)

Join community