[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]
[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]