4 citations · 5 across the 3 of their papers we have counts for
4 papers
Generative Modeling via Drifting
Mingyang Deng, He Li, Tianhong Li +2
Generative modeling can be formulated as learning a mapping f such that its pushforward distribution matches the data distribution. The pushforward behavior can be carried out iter…
Mean Flows for One-step Generative Modeling
Zhengyang Geng, Mingyang Deng, Xingjian Bai +2
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantane…
Autoregressive Image Generation without Vector Quantization
Tianhong Li, Yonglong Tian, He Li +2
Conventional wisdom holds that autoregressive models for image generation are typically accompanied by vector-quantized tokens. We observe that while a discrete-valued space can fa…
Measuring Feature Sparsity in Language Models
Mingyang Deng, Lucas Tao, Joe Benton
Recent works have proposed that activations in language models can be modelled as sparse linear combinations of vectors corresponding to features of input text. Under this assumpti…