Dr. Saining Xie
Assistant Professor of Computer Science
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Saining Xie. Every entry undergoes editorial source verification.
Scalable Diffusion Models with Transformers (DiT)
Authored the landmark ICCV 2023 paper proposing Diffusion Transformers (DiT), replacing traditional U-Nets with Transformer backbones operating on latent image patches, establishing power-law compute scaling for generative diffusion.
Full quadratic attention scaling over high-resolution token patches requires patchification or windowing to avoid compute explosion.
Aggregated Residual Transformations for Deep Neural Networks (ResNeXt)
Co-authored the seminal CVPR 2017 paper introducing ResNeXt, which introduced 'cardinality' (the size of the set of transformations) as a concrete, essential dimension that outclasses depth and width for computer vision accuracy.
Grouped convolution memory access patterns can yield lower tensor-core utilization compared to standard dense GEMMs.