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cs.LG2024
Pretraining with random noise for uncertainty calibration
Jeonghwan Cheon, Se-Bum Paik
Uncertainty calibration is crucial for various machine learning applications, yet it remains challenging. Many models exhibit hallucinations - confident yet inaccurate responses -…
cs.NE2024★ 3 cited
Neuromimetic metaplasticity for adaptive continual learning
Suhee Cho, Hyeonsu Lee, Seungdae Baek +1
Conventional intelligent systems based on deep neural network (DNN) models encounter challenges in achieving human-like continual learning due to catastrophic forgetting. Here, we…
cs.LG2024
Pretraining with Random Noise for Fast and Robust Learning without Weight Transport
Jeonghwan Cheon, Sang Wan Lee, Se-Bum Paik
The brain prepares for learning even before interacting with the environment, by refining and optimizing its structures through spontaneous neural activity that resembles random no…