2022

1. Locally Hierarchical Auto-Regressive Modeling for Image Generation Tackgeun You, Saehoon Kim, Doyup Lee, Chiheon Kim, and Bohyung Han In Annual Conference on Neural Information Processing System (NeurIPS), 2022
2. Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer Doyup Lee^*, Chiheon Kim^*, Saehoon Kim, Minsu Cho, and Wook-Shin Han In Annual Conference on Neural Information Processing System (NeurIPS), 2022
3. Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss Youhan Lee, Kyungtae Lim, Woonhyuk Baek, Byungseok Roh, Kim, Saehoon, In COLING 2022
4. Autoregressive Image Generation using Residual Quantization Doyup Lee^*, Chiheon Kim^*, Saehoon Kim, Minsu Cho, and Wook-Shin Han In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
5. Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity Byungseok Roh^*, JaeWoong Shin^*, Wuhyun Shin^*, Kim, Saehoon, In International Conference on Learning Representations (ICLR), 2022

2021

1. Automated Learning Rate Scheduler for Large-batch Training Chiheon Kim, Saehoon Kim, Jongmin Kim, Donghoon Lee, and Sungwoong Kim In arXiv preprint arXiv:2107.05855, 2021
2. Hybrid Generative-Contrastive Representation Learning Saehoon Kim, Sungwoong Kim, and Juho Lee In arXiv preprint arXiv:2106.06162, 2021

2020

1. Scalable and Order-robust Continual Learning with Additive Parameter Decomposition Jaehong Yoon, Saehoon Kim, Eunho Yang, and Sung Ju Hwang In International Conference on Learning Representations (ICLR), 2020

2019

1. Deep Gaussian Processes for Weakly Supervised Learning: Tumor Mutation Burden (TMB) Prediction Sunho Park, Saehoon Kim, Hongmin Xu, and Tae Hyun Hwang In NeurIPS 2019 Workshop on Bayesian Deep Learning, 2019
2. 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 In International Conference on Learning Representations (ICLR), 2019
3. A Deep Learning Model for Real-time Mortality Prediction in Critically-ill Children Soo Yeon Kim^*, Saehoon Kim^*, Joongbum Cho, In Suk Sol, Youngchul Sung, Inhyeok Cho, Minseop Park, Haerin Jang, Yoon Hee Kim, Kyung Won Kim, and Myung Hyun Sohn Critical Care, 2019
4. Bayesian Optimization over Sets Jungtaek Kim, Michael McCourt, Tackgeun You, Saehoon Kim, and Seungjin Choi In ICML’19 Workshop on Automated Machine Learning, 2019

2018

1. TAEML: Task-Adaptive Ensemble of Meta-Learners Minseop Park, Saehoon Kim, Jungtaek Kim, Yanbin Liu, and Seungjin Choi In NeurIPS’18 Workshop on Meta-Learning, 2018
2. Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout Juho Lee, Saehoon Kim, Jaehong Yoon, Hae Beom Lee, Eunho Yang, and Sung Ju Hwang In arXiv preprint arXiv:1805.10896, 2018
3. DropMax: Adaptive Variational Softmax Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, and Sung Ju Hwang In Annual Conference on Neural Information Processing System (NeurIPS), 2018
4. Uncertainty-Aware Attention for Reliable Interpretation and Prediction Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, and Sung Ju Hwang In Annual Conference on Neural Information Processing System (NeurIPS), 2018
5. Sparse Circulant Binary Embedding: An Asymptotic Analysis Saehoon Kim, and Seungjin Choi IEEE Signal Processing Letters, 2018
6. On the Optimal Bit Complexity of Circulant Binary Embedding Saehoon Kim, Jungtaek Kim, and Seungjin Choi In AAAI Conference on Artificial Intelligence (AAAI), 2018
7. Product Qunatized Translations for Fast Nearest Neighbor Search Yoonho Hwang, Moo-yeol Baek, Saehoon Kim, Bohyung Han, and Hee-kap Ahn In AAAI Conference on Artificial Intelligence (AAAI), 2018

2017

1. Binary Embedding with Additive Homogeneous Kernels Saehoon Kim, and Seungjin Choi In AAAI Conference on Artificial Intelligence (AAAI), 2017
2. Learning to Transfer Initializations for Bayesian Hyperparameter Optimization Jungtaek Kim, Saehoon Kim, and Seungjin Choi In NeurIPS’17 Workshop on Bayesian Optimization, 2017

2016

1. Prediction and Predictability for Search Query Acceleration Seungwon Hwang, Saehoon Kim, Yuxiong He, Sameh Elnikety, and Seungjin Choi ACM Transcations on the Web, 2016

2015

1. Bilinear Random Projections for Locality-Sensitive Binary Codes Saehoon Kim, and Seungjin Choi In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
2. DDS Prediction: Reducing Extreme Tail Latency in Web Search Saehoon Kim, Yuxiong He, Seungwon Hwang, Sameh Elnikety, and Seungjin Choi In ACM International WSDM Conference (WSDM), 2015
3. Near-Duplicate Image Discovery on One Billion Images Saehoon Kim, Xin-Jing Wang, Lei Zhang, and Seungjin Choi In IEEE Winter Conference on Applications of Computer Vision (WACV), 2015

2014

1. Predictive Parallelization: Taming Tail Latencies in Web Search Myeongjae Jeon, Saehoon Kim, Seung-won Hwang, Yuxiong He, Sameh Elnikety, L. Alex Cox, and Scott Rixner In ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2014

2013

1. Multi-view Anchor Graph Hashing Saehoon Kim, and Seungjin Choi In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013

2012

1. Hashing with Generalized Nyström Approximation Jeong-Min Yun, Saehoon Kim, and Seungjin Choi In IEEE International Conference on Data Mining (ICDM), 2012
2. Deep Learning to Hash with Multiple Representations Yoonseop Kang, Saehoon Kim, and Seungjin Choi In IEEE International Conference on Data Mining (ICDM), 2012
3. Sequential Spectral Learning to Hash with Multiple Representations Saehoon Kim, Yoonseop Kang, and Seungjin Choi In European Conference on Computer Vision (ECCV), 2012

2011

1. Semi-Supervised Discriminant Hashing Saehoon Kim, and Seungjin Choi In IEEE International Conference on Data Mining (ICDM), 2011

2010

1. Local Dimensionality Reduction for Multiple Instance Learning Saehoon Kim, and Seungjin Choi In IEEE International Workshop on Machine Learning for Signal Processing, 2010