9 citations · 13 across the 6 of their papers we have counts for
6 papers
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…
"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…
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC
Peiyu Yu, Yaxuan Zhu, Sirui Xie +4
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in the field of generative modeling due to its flexibility in the formulati…
Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
Grid cells in the entorhinal cortex of mammalian brains exhibit striking hexagon grid firing patterns in their response maps as the animal (e.g., a rat) navigates in a 2D open envi…
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells
Dehong Xu, Ruiqi Gao, Wen-Hao Zhang +2
The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal. Recurrent ne…