Young-Jin Park | DeepAI

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Hao Wang
366 publications
Jung-Woo Ha
58 publications
Kyung-Min Kim
21 publications
Navid Azizan
21 publications
Minkyu Kim
20 publications
Han-Lim Choi
18 publications
Jisu Jeong
14 publications
Jung-Su Ha
13 publications
Shervin Ardeshir
13 publications
Kashif Rasul
12 publications
Donghyun Kwak
10 publications

∙ 05/31/2023

Representation Reliability and Its Impact on Downstream Tasks

Self-supervised pre-trained models extract general-purpose representatio...

0 Young-Jin Park, et al. ∙

∙ 05/31/2022

VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting

Time series models aim for accurate predictions of the future given the ...

8 Kashif Rasul, et al. ∙

∙ 09/05/2021

Global-Local Item Embedding for Temporal Set Prediction

Temporal set prediction is becoming increasingly important as many compa...

0 Seungjae Jung, et al. ∙

∙ 05/24/2021

One4all User Representation for Recommender Systems in E-commerce

General-purpose representation learning through large-scale pre-training...

0 Kyuyong Shin, et al. ∙

∙ 11/21/2020

A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting

Probabilistic time-series models become popular in the forecasting field...

0 Seungjae Jung, et al. ∙

∙ 11/16/2020

Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning

We present a hierarchical planning and control framework that enables an...

0 Jung-Su Ha, et al. ∙

∙ 09/21/2020

div2vec: Diversity-Emphasized Node Embedding

Recently, the interest of graph representation learning has been rapidly...

0 Jisu Jeong, et al. ∙

∙ 07/05/2020

Multi-Manifold Learning for Large-scale Targeted Advertising System

Messenger advertisements (ads) give direct and personal user experience ...

0 Kyuyong Shin, et al. ∙

∙ 06/26/2020

Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments

Graph representation learning is gaining popularity in a wide range of a...

0 Young-Jin Park, et al. ∙

∙ 04/21/2020

A Neural Process Approach for Probabilistic Reconstruction of No-Data Gaps in Lunar Digital Elevation Maps

With the advent of NASA's lunar reconnaissance orbiter (LRO), a large am...

0 Young-Jin Park, et al. ∙

∙ 07/24/2019

Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation

Graph Neural Networks (GNNs) have been emerging as a promising method fo...

0 Kyung-Min Kim, et al. ∙

∙ 03/14/2019

Online Gaussian Process State-Space Models: Learning and Planning for Partially Observable Dynamical Systems

Gaussian process state-space model (GPSSM) is a probabilistic dynamical ...

0 Soon-Seo Park, et al. ∙

∙ 09/19/2018

InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics

The goal of system identification is to learn about underlying physics d...

0 Young-Jin Park, et al. ∙

∙ 07/05/2018

Adaptive Path-Integral Autoencoder: Representation Learning and Planning for Dynamical Systems

We present a representation learning algorithm that learns a low-dimensi...

0 Jung-Su Ha, et al. ∙

∙ 07/05/2018

Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems

We present a representation learning algorithm that learns a low-dimensi...

0 Jung-Su Ha, et al. ∙

∙ 06/21/2018

Deep Gaussian Process-Based Bayesian Inference for Contaminant Source Localization

This paper proposes a Bayesian framework for localization of multiple so...

0 Young-Jin Park, et al. ∙

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