21 citations · 28 across the 9 of their papers we have counts for
8 papers · 1 filter
Where the Score Lives: A Wavelet View of Diffusion
Emma Finn, Binxu Wang, T. Anderson Keller +1
Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images. A variety of architectures including CNNs,…
The two clocks and the innovation window: When and how generative models learn rules
Binxu Wang, Emma Lucia Byrnes Finn, Bingbin Liu
Generative models trained on finite data face a fundamental tension: their score-matching or next-token objective converges to the empirical training distribution rather than the p…
Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training
William Hoy, Binxu Wang, Xu Pan
Evolution Strategies (ES) have emerged as a scalable gradient-free alternative to reinforcement learning based LLM fine-tuning, but it remains unclear whether comparable task perfo…
A Random Matrix Theory Perspective on the Consistency of Diffusion Models
Binxu Wang, Jacob Zavatone-Veth, Cengiz Pehlevan
Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise seed. We trace this consistency to a…
An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models
Binxu Wang, Cengiz Pehlevan
We develop an analytical framework for understanding how the generated distribution evolves during diffusion model training. Leveraging a Gaussian-equivalence principle, we solve t…
The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and its Applications
Binxu Wang, John J. Vastola
By learning the gradient of smoothed data distributions, diffusion models can iteratively generate samples from complex distributions. The learned score function enables their gene…