3 citations · 3 across the 2 of their papers we have counts for
4 papers
Trinity: One Differentiable Physics for Training, Refining and Scoring Generative Floorplanners
Shih-Ying Yeh, Tzu-Sian Wang, Xuehai Wang +7
Floorplanning arranges the blocks of a chip and decides their shapes under objectives that press blocks together, short wirelength and a small outline, and constraints that hold th…
TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining
Shih-Ying Yeh, Daniel Z. Kaplan, Xuehai Wang +3
Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, sc…
Scaling Vision Transformers for Functional MRI with Flat Maps
Connor Lane, Mihir Tripathy, Leema Krishna Murali +15
We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using the…
Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers
Katherine Crowson, Stefan Andreas Baumann, Alex Birch +3
We present the Hourglass Diffusion Transformer (HDiT), an image generative model that exhibits linear scaling with pixel count, supporting training at high-resolution (e.g. $1024 \…