Among the achievements by UNIST researchers, the number of publications on artificial intelligence (AI) that have been published in internationally recognized conferences in 2021 has increased significantly. According to the statistics, it has more than doubled since the opening of UNIST AI Graduate School.
About 20 papers have been accepted at top-notch academic conferences, including NeurlPS, ICML, ICLR, CVRP, ICCV, ICRA (See the list below). There has been significant growth, compared to 2018 (5 papers) and 2019 (9 papers) before the opening of UNIST Graduate School.
These conferences bring together leading experts in the field of AI to share and discuss their research findings and latest ideas. Papers accepted to these conferences are highly valued both academically and industrially. Therefore, global companies, such as Google, Amazon, and Samsung Electronics consider these conferences as a place to promote R&D performance in the field of AI and recruit talents.
In 2021, researchers at UNIST have published a number of AI-related papers in leading international journals, which includes IEEE TPAMI, Nature Communications, and Information Sciences. These achievements are the result of UNIST’s steady investment in AI research and hiring AI talents.
The opening ceremony for UNIST AI Graduate School took place on November 19, 2020.
Centering on UNIST AI Graduate School, UNIST will continually deliver excellent results through active cooperation with other departments, and via the expansion of industrial-academic cooperation with domestic and foreign research institutes. Indeed, the research achievements, produced this year, were based on research cooperation among UNIST’s Graduate School of AI, the Department of Industrial Engineering, the Department of Electrical Engineering, and the Department of Computer Science and Engineering. This also includes cooperation among KAIST, Seoul National University, Naver Inc., Cornell University, Brown University, University of Bath, and Tsinghwa University.
[List of Publications by UNIST Researchers on AI in 2021]
1. CVPR 2021, “Task-aware variational adversarial active learning”, Kwanyoung Kim, Dongwon Park, Kwang In Kim, and Se Young Chun
2. CVPR 2021, “Automated Log-Scale Quantization for Low-Cost Deep Neural Networks”, Sangyun Oh, Hyeonuk Sim, Sugil Lee and Jongeun Lee
3. ICCV 2021, “Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation”, Haoang Li, Kai Chen, Pyonjin Kim, Kuk-Jin Yoon, Zhe Liu, Kyungdon Joo and Yun-Hui Liu
4. ICCV 2021, “Testing using privileged information by adapting features with statistical dependence”, Kwang In Kim and James Tompkin
5. ICCV 2021, “End-to-End Detection and Pose Estimation of Two Interacting Hands”, Donguk Kim, Kwang In Kim, Seungryul Baek
6. ICCV 2021, “End-to-end trainable trident person search network using adaptive gradient propagation”, Byeong-Ju Han, Kuhyeun Ko, and Jae-Young Sim
7. ICCV 2021, “Rethinking the truly unsupervised image to image translation”, Kyungjune Baek, Yunjey Choi, Youngjung Uh, Jaejun Yoo, Hyunjung Shim
8. ICML 2021, “Improving predictors via combination across diverse task categories”, Kwang In Kim
9. ICML 2021, “Diversity actor-critic: Sample-aware entropy regularization for sample-efficient exploration”, Seungyul Han and Youngchul Sung
10. NeurIPS 2021, “Doubly Robust Thompson Sampling with Linear Payoffs”, Wonyoung Kim, Gi-Soo Kim, Myunghee Cho Paik
11. NeurIPS 2021, “Neural Bootstrapper”, Minsuk Shin, Hyungjoo Cho, Hyun-seok Min, Sungbin Lim
12. NeurIPS 2021, “Meta-Learning Sparse Implicit Neural Representations”, Jaeho Lee, Jihoon Tack, Namhoon Lee, Jinwoo Shin
13. NeurIPS 2021, “A Max-Min Entropy Framework for Reinforcement Learning”, Seungyul Han and Youngchul Sung
14. NeurIPS 2021, “MIND Dataset for Diet Planning and Dietary Healthcare with Machine Learning: Dataset Creation using Combinatorial Optimization and Controllable Generation with Domain Experts”, Changhun Lee, Soohyeok Kim, Sehwa Jeong, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung
15. ICLR 2021, “Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training”, Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr, Martin Jaggi
16. KDD 2021, “Diet Planning with Machine Learning: Teacher-forced REINFORCE for Composition Compliance with Nutrition Enhancement”, Changhun Lee, Soohyeok Kim, Chiehyeon Lim, Jayun Kim, Yeji Kim, Minyoung Jung
17. ICDM 2021, “An Empirical Experiment on Deep Learning Models for Predicting Traffic Data”, Hyunwook Lee, Cheonbok Park, Seungmin Jin, Hyeshin Chu, Jaegul Choo, Sungahn Ko
18. WWW 2021, “Wait, Let’s Think about Your Purchase Again: A Study on Interventions for Supporting Self-Controlled Online Purchases”, Yunha Han, Hwiyeon Kim, Hyeshin Chu, Joohee Kim, Hyunwook Lee, Seunghyeong Choe, Dooyoung Jung, Dongil Chung, Bum Chul Kwon, Sungahn Ko
19. ICRA 2021, “Volumetric Propagation Network: Stereo-LiDAR Fusion for High-Quality Depth Estimation”, Jaesung Choe, Kyungdon Joo, Tooba Imtiaz and In So Kweon
20. ICRA 2021, “Stereo Object Matching Network”, Jaesung Choe, Kyungdon Joo, Francois Rameau and In So Kweon
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