14 citations · 25 across the 5 of their papers we have counts for
6 papers
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
Taehyeon Kim, Shinhwan Kang, Hyeonjeong Shin +4
The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contex…
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Jaeyeon Ahn, Taehyeon Kim, Seyoung Yun
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-sp…
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
FINE Samples for Learning with Noisy Labels
Taehyeon Kim, Jongwoo Ko, Sangwook Cho +2
Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into…
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Taehyeon Kim, Jaeyeon Ahn, Nakyil Kim +1
In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience. Therefore, automa…
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
Taehyeon Kim, Sangmin Bae, Jin-woo Lee +1
Federated learning has emerged as an innovative paradigm of collaborative machine learning. Unlike conventional machine learning, a global model is collaboratively learned while da…