1 citations · 1 across the 4 of their papers we have counts for
5 papers
NeuralRemaster: Phase-Preserving Diffusion for Structure-Aligned Generation
Yu Zeng, Charles Ochoa, Mingyuan Zhou +3
Standard diffusion corrupts data using Gaussian noise whose Fourier coefficients have random magnitudes and random phases. While effective for unconditional or text-to-image genera…
SPID: Distilled Protein Backbone Generation
Liyang Xie, Haoran Zhang, Zhendong Wang +2
Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unprecedented capabilities for de novo prot…
Controllable diffusion-based generation for multi-channel biological data
Haoran Zhang, Mingyuan Zhou, Wesley Tansey
Spatial profiling technologies in biology, such as imaging mass cytometry (IMC) and spatial transcriptomics (ST), generate high-dimensional, multi-channel data with strong spatial…
Diffusion Boosted Trees
Xizewen Han, Mingyuan Zhou
Combining the merits of both denoising diffusion probabilistic models and gradient boosting, the diffusion boosting paradigm is introduced for tackling supervised learning problems…
CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator
Alek Dimitriev, Mingyuan Zhou
Accurately backpropagating the gradient through categorical variables is a challenging task that arises in various domains, such as training discrete latent variable models. To thi…