[C31]

Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs

Dong Bok Lee*, Seanie Lee*, Woo Tae Jeong, Donghwan Kim and Sung Ju Hwang, ACL 2020

(*: equal contribution)

[C30]

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

Hae Beom Lee*, Hayeon Lee*, Donghyun Na*, Saehoon Kim, Minseop Park, Eunho Yang, Sung Ju Hwang,

ICLR 2020 (oral presentation) (*: equal contribution)

[Paper]  [Code]

[C29]

Scalable and Order-robust Continual Learning with Additive Parameter Decomposition

Jaehong Yoon, Saehoon Kim, Eunho Yang, Sung Ju Hwang, ICLR 2020

[Paper] [Code]

[C28]

Meta Dropout: Learning to Perturb Features for Generalization

Hae Beom Lee, Taewook Nam, Eunho Yang, Sung Ju Hwang, ICLR 2020

[Paper]  [Code]

[C27]

Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks

Joonyoung Yi, Juhyuk Lee, Sungju Hwang, Eunho Yang, ICLR 2020

[Paper] 

[C26]

Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang and Eunho Yang, AAAI 2020

[Paper]  

[C25]

Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming Data

Moonsu Han, Minki Kang, Hyunwoo Jung,  Sung Ju Hwang, ACL 2019 (long paper) (oral presentation)

[Paper]  [Bibtex]  [Code]

[C24]

Learning What and Where to Transfer

Yunhun Jang, Hankook Lee, Sung Ju Hwang, and Jinwoo Shin, ICML 2019

[Paper]  [Bibtex] 

[C23]

Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss

Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang and Changkyu Choi, CVPR 2019 (oral presentation)

[Paper]  [Bibtex] 

[C22]

Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning

Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang and Yi Yang, ICLR 2019

[Paper]  [Bibtex] 

[A7]

Learning to Generalize to Unseen Tasks with Bilevel Optimization

Hayeon Lee, Donghyun Na, Hae Beom Lee, Sung Ju Hwang, Aug 2019

[A6]

SVOD: Stochastic Video Object Detection

Bruno Andreis, Jeongun Ryu, Sung Ju Hwang, May 2019

[A5]

Uncertainty-Aware Deep Temporal Asymmetric Multi-task Learning

Tuan Nguyen, Hyewon Jeong, Eunho Yang, Sung Ju Hwang, May 2019

[Paper]  

[A4]

Learning Spatial Relationships for Cross Modal Retrieval

Hayeon Lee, Wonjun Yoon, Jinseok Park, Daeshik Kim, Sung Ju Hwang, Mar 2019

[Paper] 

[C21]

DropMax: Adaptive Variational Softmax

Haebeom Lee, Juho Lee, Saehoon Kim, Eunho Yang and Sung Ju Hwang, NeurIPS 2018

[Paper]  [Bibtex]  [Code]

[C20]

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

Jay Heo, Haebeom Lee, Saehoon Kim, Juho Lee, Kwangjun Kim, Eunho Yang, and Sung Ju Hwang, NeurIPS 2018

[Paper]  [Bibtex]  [Code]

[C19]

Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding

Hajin Shim, Sung Ju Hwang and Eunho Yang, NeurIPS 2018

[Paper]  [Bibtex]  

[C18]

Deep Asymmetric Multi-task Feature Learning

Haebeom Lee, Eunho Yang and Sung Ju Hwang, ICML 2018

[Paper]  [Bibtex]  [Code]

[A3]

Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare

Ingyo Chung, Saehoon Kim, Juho Lee, Sung Ju Hwang, Eunho Yang, Jun 2018

[Paper] 

[C17]

Lifelong Learning with Dynamically Expandable Networks

Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang, ICLR 2018

[Paper]  [Bibtex]  [Code]

[A2]

Adaptive Network Sparsification with Dependent Beta-Bernoulli Dropout

Juho Lee, Saehoon Kim, Haebeom Lee, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang,  May 2018

[Paper] 

[C16]

Combined Group and Exclusive Sparsity for Deep Neural Networks

Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang, ICLR 2018

[Paper]  [Bibtex]

[C15]

SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization

Juyong Kim, Yookoon Park, Gunhee Kim and Sung Ju Hwang, ICML 2017

[Paper]  [Bibtex]  [Code]

[C14]

Knowledge Transfer with Interactive Learning of Semantic Relationships

Jonghyun Choi, Sung Ju Hwang, Leonid Sigal, and Larry S. Davis, AAAI 2016 (oral presentation)

[Paper]  [Bibtex] 

[C13]

Exploiting View-Specific Appearance Similarities Across Classes for Zero-shot Pose Prediction: A Metric Learning Approach

Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, and Leonid Sigal, AAAI 2016

[Paper]  [Bibtex] 

[C12]

Taxonomy-Regularized Semantic Deep Convolutional Neural Networks

Wonjoon Goo, Juyong Kim, Gunhee Kim and Sung Ju Hwang, ECCV 2016

[Paper]  [Bibtex]  [Code]

[C11]

Asymmetric Multi-task Learning Based on Task Relatedness and Loss

Giwoong Lee, Eunho Yang and Sung Ju Hwang, ICML 2016

[Paper]  [Bibtex]  [Code]

[A1]

Hierarchical Maximum-Margin Clustering

Guang-Tong Zhou, Sung Ju Hwang, Mark Schmidt, Leonid Sigal and Greg Mori, Feb 2015

[Paper]  [Bibtex]

[C10]

Expanding Object Detector’s Horizon: Incremental Learning Framework for Object Detection in Videos

Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn, and Leonid Sigal, CVPR 2015

[Paper]  [Bibtex] 

[W5]

A Metric Learning Approach for Multi-View Object Recognition and Zero-shot Pose Estimation

Alina Kuznetsova, Sung Ju Hwang, Bodo Rosenhahn and Leonid Sigal, ICCV Workshop on Object Understanding for Interaction 2015

[Paper] 

[W4]

Interactive Semantics for Knowledge Transfer

Jonghyun Choi, Sung Ju Hwang, Leonid Sigal and Larry S. Davis, ICML Active Learning Workshop 2015

[Paper] 

[C9]

A Unified Semantic Embedding: Relating Taxonomies and Attributes

Sung Ju Hwang and Leonid Sigal, AAAI Spring Symposium on Knowledge Representation and Reasoning (KRR) 2015

[Paper]  [Bibtex] 

[W3]

A Unified Semantic Embedding: Relating Taxonomies and Attributes

Sung Ju Hwang and Leonid Sigal, NeurIPS Workshop on Learning Semantics 2014

[Paper]  

[C8]

A Unified Semantic Embedding: Relating Taxonomies with Attributes

Sung Ju Hwang and Leonid Sigal, NeurIPS 2014

[Paper]  [Bibtex] 

[C7]

Analogy-preserving Semantic Embedding for Visual Object Categorization

Sung Ju Hwang, Kristen Grauman and Fei Sha, ICML 2013

[Paper]  [Bibtex] 

[J2]

Learning the Relative Importance of Objects from Tagged Images for Retrieval and Cross-Modal Search 

Sung Ju Hwang and Kristen Grauman, International Journal of Computer Vision (IJCV), Nov 2012

[Paper]  [Bibtex]  [Code]

[W2]

Semantic Kernel Forests from Multiple Taxonomies

Sung Ju Hwang, Fei Sha and Kristen Grauman,  NeurIPS Big Data Meets Computer Vision: International Workshop on Large Scale Visual Recognition and Retrieval (BigVision) 2012 (oral presentation)

[Paper]  [Bibtex] 

[C6]

Context-Based Automatic Local Image Enhancement

Sung Ju Hwang, Ashish Kapoor, and Sing Bing Kang, ECCV 2012

[Paper]  [Bibtex]

[J1]

Reading Between the Lines: Object Localization Using Implicit Cues from Image Tags

Sung Ju Hwang and Kristen Grauman, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), June 2012

[Paper]  [Bibtex] 

[C5]

Semantic Kernel Forests from Multiple Taxonomies

Sung Ju Hwang, Kristen Grauman and Fei Sha, NeurIPS 2012

[Paper]  [Bibtex] 

[C4]

Sharing Features Between Objects and Their Attributes

Sung Ju Hwang, Fei Sha and Kristen Grauman, CVPR 2011

[Paper]  [Bibtex] 

[W1]

Sharing Features Between Visual Tasks at Different Levels of Granularity

Sung Ju Hwang, Fei Sha and Kristen Grauman, CVPR Fine-Grained Visual Categorization Workshop (FGVC) 2011

[Paper] 

[C3]

 Learning a Tree of Metrics with Disjoint Visual Features 

Sung Ju Hwang, Kristen Grauman, and Fei Sha, NeurIPS 2011

[Paper]  [Bibtex]  [Code]

[C2]

Accounting for the Relative Importance of Objects in Image Retrieval

Sung Ju Hwang and Kristen Grauman, BMVC 2010 (oral presentation)

[Paper]  [Bibtex]  [Code]

[C1]

Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags

Sung Ju Hwang and Kristen Grauman, CVPR 2010 (oral presentation)

[Paper]  [Bibtex] 

LINKS