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cs.LG2026
Temporal Difference Learning for Diffusion Models
Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu +1
Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between…
cs.LG2024
Label Alignment Regularization for Distribution Shift
Ehsan Imani, Guojun Zhang, Runjia Li +4
Recent work has highlighted the label alignment property (LAP) in supervised learning, where the vector of all labels in the dataset is mostly in the span of the top few singular v…
cs.LG2024
Improving Adversarial Transferability via Model Alignment
Avery Ma, Amir-massoud Farahmand, Yangchen Pan +2
Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a novel model alignment technique aimed at i…