1 citations · 2 across the 6 of their papers we have counts for
6 papers
Fast and Slow Variational Continual Learning
Subarnaduti Paul, Yohan Jung, Mohammad Emtiyaz Khan +3
Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural m…
Flat Posterior Does Matter For Bayesian Model Averaging
Sungjun Lim, Jeyoon Yeom, Sooyon Kim +5
Bayesian neural networks (BNNs) estimate the posterior distribution of model parameters and utilize posterior samples for Bayesian Model Averaging (BMA) in prediction. However, des…
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
Yohan Jung, Jinkyoo Park
Convolutional deep sets are the architecture of a deep neural network (DNN) that can model stationary stochastic process. This architecture uses the kernel smoother and the DNN to…
Approximate Inference for Spectral Mixture Kernel
Yohan Jung, Kyungwoo Song, Jinkyoo Park
A spectral mixture (SM) kernel is a flexible kernel used to model any stationary covariance function. Although it is useful in modeling data, the learning of the SM kernel is gener…
Implicit Kernel Attention
Kyungwoo Song, Yohan Jung, Dongjun Kim +1
\textit{Attention} computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transf…
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
Yohan Jung, Jinkyoo Park
Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission can be effectively used to estimate the hidden state with a sequence of complex input-output relational obser…