Jaesik Choi | DeepAI

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Seong-Whan Lee
75 publications
Jung-Woo Ha
58 publications
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27 publications
Se Young Chun
25 publications
Hung Bui
17 publications
Kesheng Wu
17 publications
Alex Sim
13 publications
Yunjey Choi
12 publications
Dongwon Park
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Toan Tran
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Woo-Jeoung Nam
9 publications

∙ 05/31/2023

Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning

For robotic vehicles to navigate robustly and safely in unseen environme...

0 Kyowoon Lee, et al. ∙

∙ 04/23/2023

The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations

Deep neural networks based on batch normalization and ReLU-like activati...

0 Inyoung Paik, et al. ∙

∙ 02/22/2023

Stress and Adaptation: Applying Anna Karenina Principle in Deep Learning for Image Classification

Image classification with deep neural networks has reached state-of-art ...

10 Nesma Mahmoud, et al. ∙

∙ 09/01/2022

Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?

Many contextualized word representations are now learned by intricate ne...

10 Sunjae Kwon, et al. ∙

∙ 06/17/2022

Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images

Evaluation metrics in image synthesis play a key role to measure perform...

9 Jiyeon Han, et al. ∙

∙ 02/17/2022

Variational Neural Temporal Point Process

A temporal point process is a stochastic process that predicts which typ...

0 Deokjun Eom, et al. ∙

∙ 01/17/2022

Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?

Even though image generation with Generative Adversarial Networks has be...

28 Hwanil Choi, et al. ∙

∙ 12/16/2021

An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks

Despite significant improvements on the image generation performance of ...

0 Haedong Jeong, et al. ∙

∙ 04/13/2021

Automatic Correction of Internal Units in Generative Neural Networks

Generative Adversarial Networks (GANs) have shown satisfactory performan...

0 Ali Tousi, et al. ∙

∙ 12/21/2020

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

Choosing a proper set of kernel functions is an important problem in lea...

0 Anh Tong, et al. ∙

∙ 12/07/2020

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations

The clear transparency of Deep Neural Networks (DNNs) is hampered by com...

0 Woo-Jeoung Nam, et al. ∙

∙ 10/19/2020

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems

Recent advances in Deep Gaussian Processes (DGPs) show the potential to ...

0 Anh Tong, et al. ∙

∙ 10/16/2020

Improved Predictive Deep Temporal Neural Networks with Trend Filtering

Forecasting with multivariate time series, which aims to predict future ...

0 Youngjin Park, et al. ∙

∙ 05/28/2020

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Deep Neural Network (DNN) models have continuously been growing in size ...

0 Jay H. Park, et al. ∙

∙ 04/27/2020

Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Units

Recently deep neural networks demonstrate competitive performances in cl...

6 Sohee Cho, et al. ∙

∙ 12/12/2019

An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks

Deep generative neural networks (DGNNs) have achieved realistic and high...

14 Giyoung Jeon, et al. ∙

∙ 11/16/2019

IDEALEM: Statistical Similarity Based Data Reduction

Many applications such as scientific simulation, sensing, and power grid...

0 Dongeun Lee, et al. ∙

∙ 11/08/2019

Why Do Masked Neural Language Models Still Need Common Sense Knowledge?

Currently, contextualized word representations are learned by intricate ...

0 Sunjae Kwon, et al. ∙

∙ 09/16/2019

A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection

Recently, robotic grasp detection (GD) and object detection (OD) with re...

18 Dongwon Park, et al. ∙

∙ 05/30/2019

Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes

In the analysis of sequential data, the detection of abrupt changes is i...

0 Jiyeon Han, et al. ∙

∙ 04/01/2019

Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

As Deep Neural Networks (DNNs) have demonstrated superhuman performance ...

8 Woo-Jeoung Nam, et al. ∙

∙ 05/24/2018

Hawkes Process Kernel Structure Parametric Search with Renormalization Factors

Hawkes Processes are a type of point process for modeling self-excitatio...

0 Rafael Lima, et al. ∙

∙ 03/28/2017

Discovering Explainable Latent Covariance Structure for Multiple Time Series

Analyzing time series data is important to predict future events and cha...

0 Anh Tong, et al. ∙

∙ 07/04/2016

Automatic Generation of Probabilistic Programming from Time Series Data

Probabilistic programming languages represent complex data with intermin...

0 Anh Tong, et al. ∙

∙ 02/12/2016

Global Deconvolutional Networks for Semantic Segmentation

Semantic image segmentation is a principal problem in computer vision, w...

0 Vladimir Nekrasov, et al. ∙

∙ 11/26/2015

The Automatic Statistician: A Relational Perspective

Gaussian Processes (GPs) provide a general and analytically tractable wa...

0 Yunseong Hwang, et al. ∙

∙ 10/16/2012

Lifted Relational Variational Inference

Hybrid continuous-discrete models naturally represent many real-world ap...

0 Jaesik Choi, et al. ∙

∙ 03/15/2012

Lifted Inference for Relational Continuous Models

Relational Continuous Models (RCMs) represent joint probability densitie...

0 Jaesik Choi, et al. ∙

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