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[DLMath&Efficiency] Shikang Zheng - Hybrid Feature Caching for Diffusion Transformers
Looped Diffusion Transformer: Depth Through Recurrence
Outstanding Paper Review: Diffusion Transformers with Representation Autoencoders
[CVPR2026 Highlight] DDiT: Dynamic Patch Scheduling for Efficient Diffusion Transformers
SoraCT: Unconditioned 3D CT Synthesis via Video Diffusion Transformers — J. Wang, FAU | HAICON26
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Last Updated: October 2, 2026
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Summary
In this AI Research Roundup episode, Alex discusses the paper: 'Upsample What Matters: Region-Adaptive Latent Sampling for ... In this video, we'll dive deep into Chenxi Du:Tsinghua University;Yongheng Deng:Tsinghua University;Ju Ren:Tsinghua University;Yaoxue Zhang:Tsinghua ... ... Mixed-Resolution Latent Upsampling for Spatially Today's standout paper is about a deceptively simple idea: instead of making Link to Arxiv Paper: arxiv.org/abs/2503.16057 The YouTube video discusses the research paper "Expert Race: A flexible ... Title: Let Features Decide Their Own Solvers: Hybrid Feature Caching for CVPR2026 Highlight DDiT: Dynamic Patch Scheduling for Efficient Speaker: J. Wang Affiliation: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) / Imperial College London Date: June 9, ...
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