Ching-Hua Lee — accepted AI-conference papers

2025

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior

Ching-Hua Lee, Chouchang Yang, Jaejin Cho, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen +1

ICML 2025poster

Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass ce…

Cited by 0 Source ↗PDF ↗Scholar ↗

2024

CIFD: Controlled Information Flow to Enhance Knowledge Distillation

Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Jaejin Cho, Ching-Hua Lee, Chouchang Yang, Yilin Shen +1

NeurIPS 2024poster

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model. However, the transfer suffers when the teacher model is significantly larger than the student. To overcome this, prior works have proposed training intermediat…

Cited by 2 Source ↗PDF ↗Scholar ↗

2023

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang, Yashas Malur Saidutta, Ching-Hua Lee, Yilin Shen +1

NeurIPS 2023poster

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a 0-shot way, similar…

Cited by 8 Source ↗PDF ↗Scholar ↗

2021

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D Rao

NeurIPS 2021poster

Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fu…

Cited by 9 Source ↗PDF ↗code ↗Scholar ↗