Search Results for author: Yohan Jung

Found 6 papers, 0 papers with code

Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior

no code implementations22 Oct 2022 Yohan Jung, Jinkyoo Park

To remedy this issue, we introduce Bayesian convolutional deep sets that construct the random translation equivariant functional representations with stationary prior.

Inductive Bias Time Series +1

Scalable Hybrid Hidden Markov Model with Gaussian Process Emission for Sequential Time-series Observations

no code implementations pproximateinference AABI Symposium 2021 Yohan Jung, Jinkyoo Park

We then propose a scalable learning method to train the HMM-GPSM model using large-scale data having (1) long sequences of state transitions and (2) a large number of time-series observations for each hidden state.

Time Series Variational Inference

Approximate Inference for Spectral Mixture Kernel

no code implementations12 Jun 2020 Yohan Jung, Kyungwoo Song, Jinkyoo Park

To improve the training, we propose an approximate Bayesian inference for the SM kernel.

Bayesian Inference Variational Inference

Implicit Kernel Attention

no code implementations11 Jun 2020 Kyungwoo Song, Yohan Jung, Dongjun Kim, Il-Chul Moon

For the attention in Transformer and GAT, we derive that the attention is a product of two parts: 1) the RBF kernel to measure the similarity of two instances and 2) the exponential of $L^{2}$ norm to compute the importance of individual instances.

Graph Attention Node Classification +2

Spectral Mixture Kernel Approximation Using Reparameterized Random Fourier Feature

no code implementations pproximateinference AABI Symposium 2019 Yohan Jung, Jinkyoo Park

We propose a method for Spectral Mixture kernel approximation using the Reparameterized Random Fourier Feature (R-RFF) in the sense of both general parameter and natural parameter view.

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