21 citations · 30 across the 15 of their papers we have counts for
16 papers
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…
Differentiable Faithfulness Alignment for Cross-Model Circuit Transfer
Shun Shao, Binxu Wang, Shay B. Cohen +2
Mechanistic interpretability has made it possible to localize circuits underlying specific behaviors in language models, but existing methods are expensive, model-specific, and dif…
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…
Circuit Mechanisms for Spatial Relation Generation in Diffusion Transformers
Binxu Wang, Jingxuan Fan, Xu Pan
Diffusion Transformers (DiTs) have greatly advanced text-to-image generation, but models still struggle to generate the correct spatial relations between objects as specified in th…
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…
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models
Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince +7
Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dicti…
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…
Diverse capability and scaling of diffusion and auto-regressive models when learning abstract rules
Binxu Wang, Jiaqi Shang, Haim Sompolinsky
Humans excel at discovering regular structures from limited samples and applying inferred rules to novel settings. We investigate whether modern generative models can similarly lea…
The Hidden Linear Structure in Score-Based Models and its Application
Binxu Wang, John J. Vastola
Score-based models have achieved remarkable results in the generative modeling of many domains. By learning the gradient of smoothed data distribution, they can iteratively generat…
Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later
Binxu Wang, John J. Vastola
How do diffusion generative models convert pure noise into meaningful images? In a variety of pretrained diffusion models (including conditional latent space models like Stable Dif…
On the Level Sets and Invariance of Neural Tuning Landscapes
Binxu Wang, Carlos R. Ponce
Visual representations can be defined as the activations of neuronal populations in response to images. The activation of a neuron as a function over all image space has been descr…
High-performance Evolutionary Algorithms for Online Neuron Control
Binxu Wang, Carlos R. Ponce
Recently, optimization has become an emerging tool for neuroscientists to study neural code. In the visual system, neurons respond to images with graded and noisy responses. Image…
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation
Binxu Wang, David Mayo, Arturo Deza +2
Self-supervised learning is a powerful way to learn useful representations from natural data. It has also been suggested as one possible means of building visual representation in…
The Geometry of Deep Generative Image Models and its Applications
Binxu Wang, Carlos R. Ponce
Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These netwo…