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cs.LG2024
Physics Informed Distillation for Diffusion Models
Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon +3
Diffusion models have recently emerged as a potent tool in generative modeling. However, their inherent iterative nature often results in sluggish image generation due to the requi…
cs.LG2023
ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure
Hee Suk Yoon, Joshua Tian Jin Tee, Eunseop Yoon +4
Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions. Traditionally, post-processing methods have been used to calibrate th…