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.
no code implementations • 7 Jan 2020 • Yohan Jung, Jinkyoo Park
This model can effectively model the sequence of time-series data.
no code implementations • 11 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.
no code implementations • 12 Jun 2020 • Yohan Jung, Kyungwoo Song, Jinkyoo Park
To improve the training, we propose an approximate Bayesian inference for the SM kernel.
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.
no code implementations • 22 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.