4 papers
Designing Instance-Level Sampling Schedules via REINFORCE with James-Stein Shrinkage
Peiyu Yu, Suraj Kothawade, Sirui Xie +2
Most post-training methods for text-to-image samplers focus on model weights: either fine-tuning the backbone for alignment or distilling it for few-step efficiency. We take a diff…
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Peiyu Yu, Dinghuai Zhang, Hengzhi He +10
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating r…
On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
This paper investigates the conformal isometry hypothesis as a potential explanation for the hexagonal periodic patterns in grid cell response maps. We posit that grid cell activit…
EM Distillation for One-step Diffusion Models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma +6
While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process. Existing distillation methods enable efficient sampling, bu…