| Learning to scale logits for temperature-conditional GFlowNets M Kim*, J Ko*, T Yun*, D Zhang, L Pan, W Kim, J Park, E Bengio, ... International Conference on Machine Learning (ICML), 2024 | 14 | 2024 |
| Multilevel approach to efficient gradient calculation in stochastic systems J Ko, M Poli, S Massaroli, WC Kim ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 2 | 2023 |
| A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs YY Choi, M Lee, SW Park, S Lee, J Ko The Third Learning on Graphs Conference (LoG), 2024 | 1 | 2024 |
| Layer-Adaptive State Pruning for Deep State Space Models M Gwak, S Moon, J Ko, PG Park Neural Information Processing Systems (NeurIPS), 2024 | | 2024 |
| Demystifying SGD with Doubly Stochastic Gradients K Kim, J Ko, YA Ma, JR Gardner International Conference on Machine Learning (ICML), 2024 | | 2024 |
| Provably Scalable Black-Box Variational Inference with Structured Variational Families J Ko*, K Kim*, WC Kim, JR Gardner International Conference on Machine Learning (ICML), 2024 | | 2024 |
Woo Chang KimProfessor, Industrial and Systems Engineering, KAISTAdresse e-mail validée de kaist.ac.kr